DUET: Joint Exploration of User–Item Profiles in Recommendation System
YueChen1,,YifeiSun1,,LuWang2,†,FangkaiYang2,PuZhao2,MinjieHong3,YifeiDong4,MinghuaHe2,
NanHu2,JianjinZhang2,ZhiweiDai2,YuefengZhan2,WeihaoHan2,HaoSun2,
QingweiLin2,WeiweiDeng2,FengSun2,QiZhang2,SaravanRajmohan2,DongmeiZhang2
1PekingUniversity2Microsoft3ZhejiangUniversity4KTHRoyalInstituteofTechnology
*Equalcontribution†Correspondingauthor
Abstract
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User History: |
Item History: |
User History: |
Item History: |
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• Misfits (Punk) |
• Review: |
• Misfits (Punk) |
• Review: |
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• I r o n M a iden |
" P o l i s he d P o p " |
• I r o n M a iden |
" P o l i s he d P o p " |
| Traditionalrecommendationsystemsrepresent ( M e t al ) • R e v i e w : " G r e a t ( M e t al ) • R e v i e w : " G r e a t |
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| 6202 rpA 51 ]RI.sc[ 1v10831.4062:viXra • • Parliament Funk Bass" |
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| usersanditemsasdensevectorsandlearnto |
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Parliament |
Funk Bass" |
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(Funk) |
• Meta: "80s Rock" |
(Funk) |
• Meta: "80s Rock" |
| aligntheminasharedlatentspaceforrelevance |
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Duet |
Independent |
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| estimation. RecentLLM-basedrecommenders (Joint Alignment) Generation |
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| instead leverage |
natural-language |
representa- |
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User Profile: |
Item History: |
User Profile: |
Item History: |
| tionsthatareeasiertointerpretandintegrate Enthusiast of A peak-era FUNK- Fan of HEAVY A highly rated POP- |
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FUNK and SOUL. |
ROCK album with |
METAL and |
ROCK album with |
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| withdownstreamreasoningmodules. Thispa- Values technical genre-blurring PUNK. Prefers polished |
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mastery |
creativity. |
dark themes. |
production. |
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| textual |
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| per studies how to construct effective Semantic Alignment ✘ Semantic Mismatch |
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| profilesforusersanditems,andhowtoalign |
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| themforrecommendation. Acentraldifficulty Figure1: DUETalignsrawuseranditemdatabytrans- |
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forming them |
into textual |
profiles within |
a shared se- |
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| isthatthebestprofileformatisnotknownapri- |
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| manticspace. |
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| ori: manuallydesignedtemplatescanbebrittle |
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| andmisalignedwithtaskobjectives. |
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Moreover, |
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to introduce |
semantically |
rich, human-readable |
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| generatinguseranditemprofilesindependently |
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| mayproducedescriptionsthatareindividually representationsforrecommendation(Wangetal., |
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| plausible yet semantically inconsistent for a 2025;Zhang,2024;Baoetal.,2023;Hongetal., |
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| specificuser–itempair. |
WeproposeDUET,an |
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2025a,b,c;Wangetal.,2024). |
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Anaturaldirectionis |
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| interaction-awareprofilegeneratorthatjointly |
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| toreplacelatentvectorswithtextualuseranditem |
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| producesuseranditemprofilesconditionedon |
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| profilesthatcanbeinspected,edited,andreusedby |
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| both user history |
and item evidence. |
DUET |
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| downstreamcomponentsincorporatedwithLLMs. |
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| follows a three-stage |
procedure: |
it first turns |
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| However,existingLLM-basedapproachesremain |
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| rawhistoriesandmetadataintocompactcues, |
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| then expands these cues into paired profile limited in two important ways. First, directly |
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| promptsandthengenerateprofiles,andfinally promptinganLLMwithrawuseranditemhisto- |
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| optimizesthegenerationpolicywithreinforce- |
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| riestoobtainrecommendationsoftenyieldsnoisy |
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| mentlearningusingdownstreamrecommenda- andincompletesignals,especiallywhenhistories |
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| tionperformanceasfeedback. |
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Experimentson |
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are long, |
sparse, or heterogeneous |
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(Wang et al., |
| threereal-worlddatasetsshowthatDUETcon- |
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2025). Second, |
profile-based |
methods |
typically |
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| sistentlyoutperformsstrongbaselines,demon- |
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| relyonmanuallydesignedtemplatesorhandcrafted |
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| stratingthebenefitsoftemplate-freeprofileex- |
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| attributes,whichrequiressubstantialhumanengi- |
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| plorationandjointuser–itemtextualalignment. |
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| Project code page: https://github.com/duet- neering and constrains the representation space. |
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| review/duet_code. More fundamentally, many approaches generate |
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| userprofilesanditemprofilesindependently,with- |
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| 1 Introduction |
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out modeling |
how user |
preferences |
and item se- |
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| Traditional recommendation systems represent manticsinteractatdecisiontime(Yangetal.,2023; |
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| Xietal.,2024). |
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| usersanditemsasdensevectorsandlearntoalign |
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| theminasharedlatentspaceforrelevanceestima- Toaddressthesechallenges,wepropose DUET, |
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| tion (Covington et al., 2016; Wu, 2023). While ajointuser–itemprofilegeneratorthattakesboth |
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| effective,suchembeddingsareopaque: theyoffer userhistoryanditemhistoryasinputandproduces |
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| limitedinterpretabilityandmakeitdifficulttoan- a paired set of profiles for the interaction. Cru- |
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| alyzewhyanitemisrecommended. Recentwork cially, DUET doesnotrequireprofiletemplates: it |
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| thereforeleverageslargelanguagemodels(LLMs) istrainedwithreinforcementlearningusingfeed- |
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back from downstream recommendation perfor- asseeninworkslikeCRESDUP(Chenetal.,2007)
mance,enablingittoexploreanddiscovereffective andUPCSim(Widiyaningtyasetal.,2021). While
profileformatsautomatically. Figure1illustrates foundational,thesemethodswerelimitedbytheir
whyjointprofilingmatters. Theuserhaslistened rigid,hand-engineeredfeatures. Morerecently,the
toMisfits(punk),IronMaiden(metal),andParlia- adventofLargeLanguageModels(LLMs)hasen-
ment(funk),whilethecandidatealbumisdescribed abledashifttowardsgeneratingprofilesinnatural
byreviewssuchas“polishedpop”and“greatfunk language. Studies such as KAR (Xi et al., 2024),
bass”andametatag“’80srock.” Consideringboth GPG(Zhang,2024),PALR(Yangetal.,2023),and
sides together, DUET can reconcile these signals LettinGo (Wang et al., 2025) leverage LLMs to
intoacompatibleinterpretation(e.g.,highlighting createtextualuserprofilesfrombehavioraldata. A
the user’s funk/soul affinity and the item’s funk- keylimitationoftheseapproachesistwofold: they
rockcharacter). Incontrast,independentlygener- relyonstaticorpre-definedtemplatesforprofile
atedprofilesmayamplifydifferentfacets,summa- generation,andtheyoftenfocusexclusivelyonuser
rizingtheuseras“heavymetal/punk”whilesum- profiles,neglectingtherich,expressiveinformation
marizingtheitemas“pop-rock”,whichresultingin inherentinitemsandthecomplexdynamicsofuser-
asemanticallymismatchedpairthatobscuresthe iteminteractions. Incontrast,ourworkintroduces
truerelevancesignal. anewparadigmwherebothuseranditemprofiles
DUETproceedsinthreestages. First,rawhisto- arenotfixedbutaredynamicallyexploredandop-
ries and metadata are distilled into minimal cues timizedinasharedsemanticspacetodirectlyalign
thatcapturecompactbutinformativesignals. Sec- withrecommendationperformance.
ond, the model expands cues into richer profile
prompts to generate textual profiles, allowing ex- 2.2 ReinforcementLearningforLLM-Based
plorationoveralternativeprofilestructuresandem- RecommendationSystems
phases. Third,theresultingprofilesareconsumed
ReinforcementLearning(RL),particularlythrough
bydownstreamrecommenders,andtheirtaskfeed-
techniqueslikeRLHF,hasbecomeacoremethod
backisusedtooptimizetheprofilegenerationpol-
for aligning LLMs with specific objectives. This
icy. By coupling both sides in a shared semantic
approach has been adapted for recommendation
space,DUETlearnsuserprofilesthatreflectwhat
tasks (Wang et al., 2025; Lin et al., 2025; Deng
kindsofitemsauserprefersanditemprofilesthat
etal.,2025),butexistingeffortsoftenfacekeylimi-
reflectwhatkindsofusersanitemappealsto.
tations. Theyfrequentlyrelyonofflinerewardmod-
Ourcontributionsareasfollows:
els(Jeongetal.,2023;Chenetal.,2025;Heetal.,
• Werepresentusersanditemsasnatural-language 2025; Liu et al., 2025) that do not adapt in real-
profiles and align them in a shared semantic timetosystemfeedback,asetupthatrisksissues
space,extendingtheclassicvector-basedalign- like reward hacking (Skalse et al., 2025). Other
mentprincipletointerpretabletextualrepresenta- methods(Sunetal.,2024;Luetal.,2024)restrict
tions. themselvestoofflinepreferencetuning(e.g.,DPO),
• We introduce an exploration-based framework which can easily overfit on static datasets. Our
thatstartsfromcue-basedinitialization,expands framework, DUET,overcomesthesechallengesby
cuesintocandidateprofiles,andjointlyoptimizes integrating RL into a closed-loop system where
user and item profiles with downstream recom- downstreamrecommendationperformanceserves
mendationfeedbackviareinforcementlearning, asthereal-timerewardsignal,allowingforthedy-
avoidingrigidtemplates. namicandinteractiverefinementoftextualprofiles.
• Extensiveexperimentsacrossmultiplereal-world
datasets show that DUET consistently outper- 3 Method
formsstrongbaselines,validatingbothjointpro-
filingandfeedback-drivenprofileoptimization. DUET is a closed-loop framework that trans-
forms raw user–item interaction histories into
2 RelatedWork performance-aligned textual profiles through
learnedrepresentationstrategies. AsshowninFig-
2.1 ProfilesinRecommendation
ure 2, DUET consists of three stages. (1) Cue-
Earlyrecommendationsystemsprimarilyreliedon Based Initialization distills interaction histories
pre-definedprofilesbasedonstructuredattributes, intoconciseevidence-basedcues. (2)JointExplo-
CUE-BASED INITIALIZATION JOINT EXPLORATION ON-POLICY OPTIMIZATION
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| Initial CUE |
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| Rated: Baba Is You (5★) |
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| User: “Prefers retro puzzle games” |
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| Reviewed: “I love logic- |
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| Item: “Retro-style indie puzzle game” |
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based puzzle games.” |
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Profile Prompt |
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| Rated: The Witness (5★) |
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| User: “Describe the user's typical |
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Rated: Fez (4★) |
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puzzle-game preferences … |
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𝑃𝑟𝑜𝑓𝑖𝑙𝑒1 𝑃𝑟𝑜𝑓𝑖𝑙𝑒2 |
… |
𝑃𝑟𝑜𝑓𝑖𝑙𝑒𝑛 |
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| Item: “… item's core mechanics… |
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Profile |
Downstream Task Env |
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User |
User: “A player who enjoys logic- |
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| UserHistory |
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intensive indie puzzle games, |
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Category: Indie Puzzle |
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Ranking correctness |
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| prefers retro visual aesthetics…” |
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Game |
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&format Reward |
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| Reviews: |
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| Item: “A retro-style indie puzzle |
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“Challenging logic |
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game featuring challenging |
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Group Computation |
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mechanics.” |
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logic mechanics that appeal to |
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“ R e t r o p ix |
e l- a rt st y l e . ” |
hardcore puzzle players.” |
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𝐴𝑑𝑣1 𝐴𝑑𝑣2 |
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“ H a r d b u |
t re w a r d i n g . ” |
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… |
𝐴𝑑𝑣𝑛 |
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Figure2: |
OverviewoftheDUETframework. |
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| ration via Adaptive Profile Prompt Discovery 3.2 Cue-BasedInitialization |
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| jointly |
explores |
user-item’s |
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profile prompts |
that |
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Raw user |
histories |
and item |
metadata |
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be writ- |
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| noisy,redundant,andnotdirectlysuitableforpro- |
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| ten. (3)OptimizationviaOn-policyExploration |
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fileconstruction. |
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Toaddressthis,DUETintroduces |
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| jointlyoptimizesuseranditemprofilesunderdown- |
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| theconceptofcues: |
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| concisehypothesesthatsum- |
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| streamrecommendationfeedback. |
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marize minimal |
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| All three |
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realized |
through |
a single |
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and items. |
These |
cues act |
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cueextraction,self-prompt |
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| systemcansubsequentlyexplorericherprofilefor- |
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| inasinglesequence-to-sequencegenerationpass |
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| atinferencetime,enablingefficientdeployment. |
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Definition1(Cue). |
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Acueisaminimaltextualhy- |
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| 3.1 ProblemFormulation |
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| pothesisderivedfromhistoricaldatathathighlights |
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| WeformulateprofilegenerationinDUETasanon- one potential aspect of a user’s preference or an |
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| policyreinforcementlearningproblem,motivated item’scharacteristic. Ratherthanaimingforcom- |
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| bytheabsenceofanytextualgroundtruthdefining pleteness, cues capture partial but salient signals |
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| an optimal user or item profile. Profile quality is thatserveasstartingpointsforprofileexploration. |
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| evaluatedsolelybyitsfunctionalutilityinafixed |
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To extract |
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LLM is |
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prompted |
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minimal |
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but informative |
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| DUET |
is modeled |
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policy |
π θ in- |
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aspectsofuseroritemdata. |
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Forexample,givena |
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| user’sinteractionhistory,themodelisguidedwith |
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| For each |
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the |
state is |
defined as |
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| instructionssuchas: |
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| s = {H |
,H },whereH |
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denotetheuser |
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| spondstoasingle-passjointgeneration |
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| ences,ratingbehavior,reviewsentimentor |
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anyotherdimension. |
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------------ |
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------------------ |
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| tory, (S |
u ,S i ) are |
constructed |
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profile |
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| prompts,and(P ,P )arethefinaltextualprofiles. conciseandavoidfullsentences.” |
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s)definesajointdistribution |
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| ---------- |
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-------------------------------- |
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| over the entire generation sequence. As an on- This lightweight guidance allows the LLM to |
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| policy agent, DUET is optimized using rewards maprawhistoriesandmetadataintocompacttex- |
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| from its own sampled generations rather than by tualcues. Thedetaildexampleofcuecanbefound |
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| imitatingfixedsummaries. |
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inAppendixA.3. |
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| ------------------------ |
--- |
--- |
--- |
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-------------- |
--- |
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--- |
--- |
--- |
| 3.3 |
JointExplorationviaAdaptiveProfile |
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Single-PassInput |
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| PromptDiscovery Task:Generatestructuredprofilesfortheuseranditemfrom |
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| historicaldata. |
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| Aftercueextraction,DUETdoesnotdirectlysum- Combined Context: [User History H u ] + [Item |
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History |
H i ]+[Avg |
Ratings] |
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| ------------------------------------ |
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| marizeuserinterestsoritemattributes. |
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Instead,it |
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| “First,extractaconciseCueforbothuseranditem.Second, |
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strate- |
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| basedonthecues,constructaProfilePromptthatdefines |
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| gies—natural-languagepromptsthatdefinethefor- thedescriptionlogic.Finally,generatetheProfileguidedby |
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| theconstructedProfilePrompt.” |
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| mat,abstractionlevel,andattributeselectionlogic |
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| usedtogenerateprofiles. |
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Explorationistherefore |
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| -------------------------------------------- |
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| performedoverhowprofilesshouldbeconstructed, |
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Single-PassOutput |
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| ratherthanoversuperficialtextualparaphrases. |
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| Cue:“prefersnostalgiclogic-basedchallenges” |
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| For each user–item pair, DUET intro- Profileprompt: “Focuson1990svisualaestheticsand |
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| duces |
an |
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the |
strategicdepth.” |
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Profile: |
“A player |
who |
seeks |
retro-style |
visual charm |
| constructed_prompt,whichservesasadiscrete |
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| pairedwithdeepstrategicreasoning.” |
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| and |
interpretable |
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prompt |
S. |
This profile |
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| Cue:“retro-styleindiepuzzlewithhighdifficulty” |
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| “Describetheuser’stypicalgamingpreferencesand |
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Profile |
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“Describe |
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graphics and |
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| engagementpatterns”)thatspecifieswhataspects intellectualdifficultytomatchlogicpreference.” |
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| Profile:“A2Dexperiencefeaturingpixelatednostalgia |
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| to |
describe |
and |
how to |
organize |
them, |
without |
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| --- |
-------- |
--- |
------ |
-------- |
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| andchallengingmechanicsthatdemandlogicaldeduction.” |
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| containingtheprofilecontentitself. |
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Conditioned |
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| ---------------------------------- |
--- |
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| onS,themodelgeneratesthefinaluseranditem Figure3: Single-passgenerationinDUET: cueextrac- |
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| profiles. |
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tion,profileprompt(constructedprompt),andprofile |
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| --------- |
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------------------------------------------------ |
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| generationareproducedinonepassforbothuserand |
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| Exploration |
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is driven |
by treating |
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the profile |
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| ----------- |
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| item. |
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| promptS |
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asastochasticactionsampledfromthe |
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| ------- |
--- |
--------------------------------- |
--------------------------- |
--- |
--- |
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| policyπ |
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(S |
Cue). |
Duringtraining,theagentsam- |
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| θ |
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| plesdifferentprofilepromptinstructions,executes |
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andintellectualdifficulty”tomatch. |
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RLreinforces |
| ---------------------------- |
--- |
--- |
--- |
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---------------- |
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---------------------------------- |
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------------ |
| themtogenerateprofilepairs(P |
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,P ),andreceives |
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| thissharedsemanticdirection,suppressingirrele- |
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| a reward |
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based |
on downstream |
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recommendation |
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| --------- |
--- |
----------------------------------- |
------------- |
--- |
-------------- |
--- |
------------ |
------------- |
-------- |
--- |
----- |
---------------- |
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|
vant signals |
and |
forcing |
the |
final |
profiles to con- |
| accuracy. |
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Thepolicyisoptimizedtoreinforcepro- |
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verge |
into a shared |
semantic |
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space |
of nostalgia |
| filepromptsthatconsistentlyyieldhigherrewards, |
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| andlogic,significantlyimprovingrecommendation |
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| enablingthemodeltoactivelysearchforeffective |
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| profileconstructionformats. |
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| Thisprocessisrealizedasaunifiedgeneration |
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| 3.4 OptimizationviaOn-policyExploration |
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| pass, |
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On-policy |
optimization. |
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We train |
the profile |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
--------- |
------------- |
--- |
--- |
-------- |
----------- |
| O = [ Cue → profilepromptS → Profile], generator π in an on-policy manner against a |
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(cid:124)(cid:123)(cid:122)(cid:125) |
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(cid:124) (cid:123)(cid:122) (cid:125) |
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θ |
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| --- |
------------------------------------ |
--- |
--------- |
------------------ |
--------- |
-------------------------------------- |
--- |
--- |
--- |
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(cid:124) |
(cid:123)(cid:122) |
(cid:125) |
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| Context ProfileConstructionPrompt Execution frozendownstreammodelf, whichservesasthe |
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(1) |
environment |
critic. |
For |
each |
sampled |
user–item |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
----------- |
------- |
--- |
---- |
------- |
--------- |
| wheretheprofilepromptS parameterizesthepro- pair (u,i), the policy generates (P ,P ) and re- |
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| filegenerator. Atinferencetime,DUETcollapses ceivesascalarrewardbasedonthepredictionac- |
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| thelearnedprofilepromptdistributionandgreed- curacy of f(P ,P ). The policy parameters are |
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| ilyexecutestheoptimalprofilepromptinasingle updatedtoreinforcegenerationsthatleadtolower |
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| forwardpass,introducingnoadditionallatency. |
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predictionerror. |
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| ------------------------------------------- |
--- |
---------- |
------ |
------------ |
-------- |
------- |
--------------------------- |
--- |
--- |
--- |
--- |
------------- |
| Example |
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of profile |
prompt |
Exploration. |
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As |
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Continuousfractionalreward. |
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Usingdiscrete |
| illustrated |
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in Figure |
3, |
DUET |
resolves |
this by |
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| integerratingsasrewardsleadstosparseandunsta- |
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| identifying |
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a shared |
context |
in |
a single |
forward |
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| ----------- |
--- |
-------- |
------- |
--- |
-------- |
------- |
--- |
--- |
--- |
--- |
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--- |
| blefeedback,asnear-misspredictions(e.g.,predict- |
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| pass. ThepolicyextractsacoordinatedCue(e.g., |
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| ing4foragroundtruth5)receivethesamepenalty |
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| “prefersretropuzzlegames”)andconstructsapro- |
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assevereerrors. |
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Toprovidedenseandinformative |
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| ---- |
------ |
---- |
------------------- |
--- |
--- |
-------- |
--------------- |
--- |
---------------------------- |
--- |
--- |
--- |
| file |
prompt |
(the |
constructed_prompt) |
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that in- |
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| feedback,wedefineacontinuousfractionalreward: |
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| structsthemodeltohighlight“nostalgicaesthetics |
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| and |
strategic |
logic” |
for |
the user, |
while |
simulta- |
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| ------- |
---------- |
------ |
---------- |
---------- |
----- |
-------- |
--- |
------- |
--- |
---- |
------ |
-------- |
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y −yˆ |
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R (u,i) |
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= 1− |
ui |
ui , (2) |
| neously |
describing |
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the item’s |
“pixel-art |
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graphics |
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perf |
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| M |
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where yˆ ui = f(P u ,P i ) is the predicted relevance ingsettingbasedondownstreampredictedrating
score and M is the maximum rating gap (e.g., scores. For each observed user–item interaction,
M = 4 for a 1–5 scale). This reward provides we randomly sample nine items that the user has
fine-grainedgradientsthatencourageincremental not interacted with to form a candidate set of ten
improvementsinrecommendationaccuracy. items, which are ranked in descending order ac-
We adopt Group Relative Policy Optimization cordingtotheirpredictedratings. Theground-truth
(GRPO) (DeepSeek-AI et al., 2025) to optimize interacted item is treated as the only positive in-
theprofilegeneratorundertheabovereward. The stance,andweadoptNDCG@Kastheevaluation
downstreamrecommenderf isfrozenthroughout metric, with K set to 1, 5, and 10, to assess how
training, which yields a stable policy optimiza- effectivelythelearnedprofilessupportcorrectitem
| tionsettingandpreventsrewarddriftorfeedback- |
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ranking. |
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Baselines |
We compare |
our method |
with |
several |
| inducedrepresentationcollapse. |
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representative |
baselines. |
10H |
directly |
uses the |
| --- |
--- |
--- |
--- |
-------------- |
---------- |
--- |
-------- |
-------- |
| 4 Experiment mostrecentinteractionhistoriesforpredictionwith- |
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outconstructingexplicitprofiles. |
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KAR(Xietal., |
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| --- |
--- |
--- |
--- |
-------------------------------- |
--- |
--- |
------------ |
--- |
| 4.1 ExperimentalSettings |
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| 2024)augmentsrecommendationmodelswithex- |
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| Datasets. Experiments were conducted on three ternalreasoningknowledgeaboutuserpreferences |
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| widelyusedreal-worlddatasets. AmazonMusic andfactualknowledgeaboutitemsextractedfrom |
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| (Music) and Amazon Book (Book) are derived LLMs,whicharetransformedintotask-compatible |
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| fromtheAmazonProductdataset1,whileYelpis representations. RLMRec(Renetal.,2024)lever- |
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| fromtheYelpOpendataset2. Alldatasetsinclude agesLLMstolearnsemanticuseranditemrepre- |
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| userreviews,ratings,andrichtextualinformation. sentationsfromtextualsignalsandalignsthemwith |
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| WeusedthefullAmazonMusicdataset,butonly collaborativerelationalinformationthroughcross- |
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| subsets (latest two months for Book and six for viewrepresentationlearning. PALR(Yangetal., |
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| Yelp). Data was split by timestamp into training, 2023)fine-tunesalargelanguagemodelasarank- |
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| validation,andtestsetstopreventinformationleak- ing component that selects preferred items from |
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| age(Jietal.,2023). retrievedcandidatesexpressedinnaturallanguage. |
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| EvaluationMetrics LG (LettinGo) (Wang et al., 2025) explores di- |
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| For each observed user–item interaction (i.e., verseuserprofilecandidateswithLLMsandaligns |
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| instance based on the user’s interaction history tionperformanceviapreferenceoptimization. Rea- |
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| strictly prior to the corresponding timestamp. son4Rec(Fangetal.,2025)introducesadelibera- |
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| beforethecurrentinteractionareusedtogenerate explicitstep-wisereasoningoveruserpreferences |
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| auserprofile,whilehistoricalreviewsfromother toguideratingprediction. |
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to generate |
an item profile. |
The |
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| -------------- |
----------- |
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--- |
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| 4.2 MainResults |
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| downstreamrecommendationsystemthenpredicts |
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| theratingofthetargetitemconditionedontwoin- Table 1 presents a comparison of our proposed |
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| puts: thegenerateduserprofile,andthegenerated method against five baselines on three datasets: |
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Amazon |
Music, Amazon |
Books, |
and |
Yelp. We |
| ------------ |
------------------------------- |
--- |
--- |
------ |
------------- |
------ |
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-------- |
| itemprofile. |
Anexceptionisthe10Hbaseline,for |
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| which the downstream model directly consumes use Qwen3-8B (Team, 2025) and LLaMA3-8B |
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| therawrecentinteractionhistories. (Dubey et al., 2024) as both the profile generator |
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| andthedownstreamrecommendationmodel,with |
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| We evaluate |
performance |
using four |
widely |
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| --------------- |
----------- |
------------------ |
------- |
------------ |
----------- |
----- |
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------- |
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a prediction |
temperature |
of 0. |
In addition |
to rat- |
| adopted metrics |
(Wang |
et al., 2025; Fang |
et al., |
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| 2025): MeanAbsoluteError(MAE),RootMean ingpredictionresults,wealsoreportranking-based |
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| SquareError(RMSE),Accuracy,andF1score. evaluation results in Table ??, which assess the |
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| effectivenessofthelearnedprofilesfromadown- |
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| thegenerateduseranditemprofilesunderarank- streamrankingperspective. |
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Overallsuperiorityoverstrongbaselines. |
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As |
| --- |
--- |
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-------------------------------------- |
--- |
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| 1https://cseweb.ucsd.edu/~jmcauley/datasets/ shown in Table 1, our method consistently out- |
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| amazon/links.html |
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performs |
the strongest |
baselines |
across |
all three |
| --- |
--- |
--- |
--- |
-------- |
------------- |
--------- |
------ |
--------- |
| 2https://business.yelp.com/data/resources/ |
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| open-dataset/ |
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datasetsunderbothQwen3-8BandLLaMA3-8B. |
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| ------------- |
--- |
--- |
--- |
-------------------------------------- |
--- |
--- |
--- |
--- |
Method Yelp AmazonMusic AmazonBooks
MAE RMSE Acc F1(%) MAE RMSE Acc F1(%) MAE RMSE Acc F1(%)
(%) (%) (%)
Qwen3(8B)
10H 1.1235 1.9478 23.17 27.54 0.9102 1.4021 39.26 46.58 0.9314 1.4527 37.63 45.19
KAR(Xietal.,2024) 0.7396 1.2184 55.34 48.67 0.7483 1.1380 58.65 60.29 0.7098 1.0923 56.17 58.78
RLMRec(Renetal.,2024) 0.8197 1.3312 47.15 42.46 0.7438 1.1069 54.89 57.65 0.7812 1.1584 52.86 55.93
PALR(Yangetal.,2023) 0.7994 1.2876 48.53 43.19 0.6075 0.9531 57.35 56.77 0.7485 1.1187 54.24 56.38
LG(Wangetal.,2025) 0.6632 1.1047 56.18 48.95 0.4737 0.8834 62.37 57.09 0.5821 0.9416 59.35 60.57
R4Rec(Fangetal.,2025) 0.7028 1.1523 55.69 47.73 0.5654 0.9635 58.69 54.67 0.6397 1.0098 58.47 56.84
Ours 0.5126 0.9485 61.23 55.18 0.3937 0.7564 67.96 63.89 0.4612 0.9089 64.38 59.27
LLaMA3(8B)
10H 1.0864 1.9532 22.09 27.30 0.7917 1.3346 38.13 46.87 0.8064 1.3866 37.15 45.27
KAR(Xietal.,2024) 0.6427 1.1668 54.51 47.98 0.5726 0.9033 57.53 59.92 0.5892 0.9614 55.87 58.21
RLMRec(Renetal.,2024) 0.7428 1.3572 46.74 42.11 0.6076 0.9886 53.78 57.42 0.6226 0.9477 52.12 55.79
PALR(Yangetal.,2023) 0.7238 1.3265 47.72 43.29 0.5823 0.9222 56.73 59.31 0.5977 0.8855 55.06 57.62
LG(Wangetal.,2025) 0.6196 1.1289 56.03 51.24 0.5204 0.9369 61.92 59.50 0.5543 0.7967 58.95 60.39
R4Rec(Fangetal.,2025) 0.7586 1.0418 55.80 53.00 0.5442 0.7722 60.86 54.88 0.6029 0.8345 59.70 56.35
Ours 0.5367 0.9687 60.87 54.74 0.4680 0.8277 63.30 60.60 0.5092 0.9500 63.42 58.12
Table1: PerformanceonthreedatasetsusingQwen3(8B)andLLaMA3(8B).
Method Yelp AmazonMusic AmazonBooks
NDCG@1 NDCG@5 NDCG@10 NDCG@1 NDCG@5 NDCG@10 NDCG@1 NDCG@5 NDCG@10
10H 0.1823 0.2815 0.4928 0.1875 0.3796 0.5153 0.1841 0.3146 0.4263
KAR(Xietal.,2024) 0.2156 0.3298 0.5412 0.3018 0.4896 0.6015 0.2965 0.4715 0.5834
RLMRec(Renetal.,2024) 0.2419 0.3472 0.5587 0.3371 0.5434 0.6162 0.2748 0.4526 0.5719
PALR(Yangetal.,2023) 0.2494 0.3563 0.5691 0.3395 0.5247 0.6115 0.2627 0.4634 0.5538
LG(Wangetal.,2025) 0.3187 0.4685 0.5814 0.4012 0.5674 0.6489 0.3795 0.5189 0.6284
R4Rec(Fangetal.,2025) 0.2575 0.3792 0.5526 0.2928 0.5912 0.6343 0.3013 0.4928 0.5959
Ours 0.3390 0.4873 0.6008 0.5123 0.6165 0.7025 0.4288 0.5638 0.6599
Table2: RankingperformanceunderEASE-based(Steck,2019)hardnegatives.
Under Qwen3-8B, our method achieves an accu- our approach reaches an NDCG@1 of 0.5619
racyof61.23%onYelp,67.96%onAmazonMu- and an NDCG@10 of 0.7443, clearly outper-
sicand64.38%onAmazonBooks,surpassingLG forming the strongest baseline methods. Similar
(LettinGo)(Wangetal.,2025)by5.05%,5.59% trendsareobservedonAmazonMusic,whereour
and5.03%,respectively. Similarimprovementsare methodattains0.5347atNDCG@1and0.7331at
observed under LLaMA3-8B, indicating that the NDCG@10, as well as on Amazon Books with
gainsarestableacrossbackbonemodels. NDCG@1 of 0.4866 and NDCG@10 of 0.7107.
Theseresultsindicatethatthelearnedprofilesen-
Advantages over fixed-structure and delib-
able more accurate identification and ordering of
erative baselines. Compared with strong fixed-
relevantitemswithincandidatesets,leadingtosu-
structureordeliberativemethodssuchasKAR(Xi
perior ranking quality in downstream recommen-
etal.,2024)andReason4Rec(Fangetal.,2025),
dation. Wefurtherevaluaterankingperformance
our approach consistently achieves lower predic-
under a more challenging setting using EASE-
tion error and higher accuracy. For example, on
based (Steck, 2019) hard negatives, and observe
Amazon Books with Qwen3-8B, our method re-
consistentimprovements;detailedresultsarepro-
duces MAE to 0.4612, compared to 0.7098 for
videdinAppendixB.2.
KARand0.6397forReason4Rec,demonstrating
moreeffectiveabstractionofuserpreferenceswith-
4.3 AblationStudy
outrelyingonpredefinedreasoningtemplates.
Consistent improvements in downstream Effectivenessofprofilegeneration,cue&strategy,
ranking. Table??reportstherankingresultsun- anduser–itemjointoptimization. Table3reports
der the Qwen3-8B backbone. Our method con- the ablation results under different design con-
sistentlyachievesthebestperformanceacrossall figurations. Starting from the history-only base-
three datasets and evaluation cutoffs. On Yelp, line (10H), introducing explicit profile genera-
| Method |
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Yelp |
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AmazonMusic |
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AmazonBooks |
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MAE |
RMSE |
Acc |
F1 MAE |
RMSE |
Acc |
F1 |
MAE RMSE |
Acc F1 |
| 10H(HistoryOnly) 1.1235 1.9478 23.17 27.54 0.9102 1.4021 39.26 46.58 0.9314 1.4527 37.63 45.19 |
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| Profile 0.7218 1.1863 55.48 48.09 0.6597 1.0218 58.67 57.48 0.6764 1.0469 57.14 57.68 |
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| Profile+Cue&Strategy 0.7085 1.1654 55.83 48.54 0.5708 0.9897 58.91 55.53 0.6389 1.0108 58.43 56.88 |
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| Profile+JointOpt.(LG(Wang 0.6632 1.1047 56.18 48.95 0.4737 0.8834 62.37 57.09 0.5821 0.9416 59.35 60.57 |
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| Profile+Cue&Strategy+ 0.5126 0.9485 61.23 55.18 0.3937 0.7564 67.96 63.89 0.4612 0.9089 64.38 59.27 |
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| JointOpt.(Ours) |
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| Table3: AblationstudyondifferentdesignconfigurationsinDUETusingQwen3(8B). |
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| Method |
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Yelp |
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AmazonMusic |
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AmazonBooks |
|
| ------ |
-------- |
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----------- |
--------- |
|
MAE RMSE |
|
Acc |
F1(%) |
MAE |
RMSE |
Acc |
F1(%) |
MAE |
RMSE |
Acc F1(%) |
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(%) |
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(%) |
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| 10H+30P 0.5126 0.9485 61.23 55.18 0.3883 0.7494 67.96 63.89 0.4612 0.9089 65.13 59.97 |
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| 10H+50P 0.4909 0.9207 62.43 56.24 0.3924 0.7543 67.88 63.87 0.4553 0.9023 64.62 59.52 |
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| 10H+70P 0.4987 0.9326 61.98 55.81 0.3937 0.7564 68.22 64.12 0.4608 0.9068 64.38 59.27 |
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| Table4: Impactofhistoricalinteractionlengthonprofilequality(usingQwen3(8B)). |
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| tionyieldssubstantialperformanceimprovements jointoptimizationarecombined. Underthisfull |
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| across all datasets. For example, on Yelp, MAE configuration, accuracy reaches 61.23% on Yelp, |
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| isreducedfrom1.1235to0.7218andaccuracyin- 67.96%onAmazonMusic,and64.38%onAma- |
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| creases from 23.17% to 55.48%, confirming that zon Books, with corresponding MAE values of |
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| textualprofilesprovidesignificantlymoreinforma- 0.5126, 0.3937, and 0.4612, respectively. Com- |
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| tiverepresentationsthanrawinteractionhistories. pared with the LettinGo-style configuration, this |
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setting |
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across all |
| ------ |
---------------- |
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-------------------------------------- |
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| Adding |
the cue&strategy |
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on |
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Althoughthenumerical |
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| ------------------- |
--- |
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-------------------- |
--- |
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------------- |
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-------- |
--------- |
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the |
effectiveness |
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user–item |
optimization |
| improvementsintroducedbystrategyalonearelim- |
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| ited, these results suggest that strategy discovery byprovidingastructuredspaceforexplorationand |
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quality. |
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the |
impact of histori- |
| --- |
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-------- |
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| Whenenablingjointoptimizationwithoutstrat- cal interaction length on profile quality. Across |
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the LettinGo |
(Wang |
et |
al., 2025)-style |
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| ---------- |
------------ |
----- |
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---------------- |
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| configuration),performanceimprovesmorenotice- interactions leads to only moderate performance |
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| ablyacrossdatasets. OnAmazonMusic,accuracy differences, indicating that our method does not |
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strongly |
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In particular, |
| --- |
--- |
--- |
--- |
--- |
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-------- |
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| increasesto62.37%,comparedto58.67%withpro- |
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| filegenerationalone. Inourimplementation,this usingamoderatehistorylength(e.g.,30–50inter- |
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| settingcorrespondstoareproductionofLettinGo, actions)alreadyachievescompetitiveorbestper- |
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| whereprofilegenerationandjointoptimizationare formanceonmostmetrics,whilefurtherincreasing |
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Whiletheorig- |
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| ------------------------------- |
------- |
--------- |
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--- |
--- |
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--- |
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| inal method |
focuses |
primarily |
on |
user |
profiling, |
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| OnYelpandAmazonBooks,extendingthehis- |
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| profilestoensureafaircomparison. improveaccuracyorF1scoreandmayslightlyde- |
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| is achieved when cue&strategy discovery and toricalinteractionscanintroducenoisyorlessrele- |
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vantsignals. Incontrast,AmazonMusicexhibits maintaining comparable coverage. Compared to
relativelystableperformanceacrossdifferenthis- existing methods, which either rely on extractive
torylengths,indicatingthatuserpreferencesinthis compression(e.g.,KAR(Xietal.,2024))orgener-
domainarelesssensitivetohistorytruncation. atefree-formdescriptionswithweakergrounding
Necessity of RL-based Optimization. We com- (e.g.,RLMRec(Renetal.,2024)andPALR(Yang
pare the full model with a variant without RL etal.,2023)), DUET consistentlyattainsstronger
(DUET W/O RL) to isolate the effect of RL. As alignment without sacrificing coverage. For in-
showninTable5,removingRLleadstosubstantial stance,LG(Wangetal.,2025)achievesrelatively
performancedegradationacrossalldatasets(e.g., highalignmentbutexhibitslessstablegrounding
YelpAccuracydropsfrom61.23%to48.53%),indi- acrossdatasets,whileRLMRecattainshighercov-
catingthatthegainscannotbeattributedtoprompt erageatthecostofweakersemanticalignment. In
| designalone. |
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contrast,DUETmaintainsbothhighalignmentand |
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| Without RL, the generator reduces to a static mid-to-highcoverage,suggestingamoreeffective |
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| mapping from interaction history to textual pro- balancebetweensemanticabstractionandevidence |
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| files,lackingadaptiveselectionofrelevantsignals. |
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preservation. |
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| ------------------------------------------------ |
--- |
--- |
--- |
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------------- |
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| Incontrast,RLenablesoptimizingprofileconstruc- |
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| 4.5 CaseStudy |
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| tionunderrewardfeedback,resultinginmoredis- |
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| Figure4presentsarepresentativecasestudythat |
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| criminative |
representations.These |
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demon- |
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| ----------- |
--------------------- |
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| strate that RL-based optimization is essential for demonstrates how semantically aligned user and |
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| DUET |
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itemprofiles |
enableaccurate |
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prediction |
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thattran- |
| ---- |
--- |
--- |
--- |
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------------ |
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---------- |
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| scendsthelimitationsofsparserawinteractionhis- |
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| 4.4 SemanticAnalysisofGeneratedProfiles tories. In the user profile, initially scattered and |
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| To better understand the source of performance fragmentedpreferencecuesaresystematicallydis- |
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| gains, we analyze whether the generated profiles tilled into a stable and coherent preference struc- |
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| exhibitmeaningfulsemanticpropertiesratherthan turefavoringfunk,soul,andprogressiverock(or- |
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| servingasintermediatetextualartifacts. Weintro- ange), coupled with a pronounced emphasis on |
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| duce two complementary metrics to characterize musicalcomplexityandhistoricalsignificance(pur- |
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| semanticcompatibilityandgrounding. ple). Thecorrespondingitemprofileexhibitsstrik- |
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| Semantic Alignment. We measure the ingsymmetry,characterizingthealbumasanexem- |
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| embedding-levelsimilaritybetweengenerateduser plaryfunk-rockcomposition(orange)andposition- |
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| anditemprofilesusingall-mpnet-base-v2from ingitasadefiningandculturallyinfluentialrelease |
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| Sentence-Transformers (Reimers and Gurevych, of the 1970s (purple). This example shows 519 |
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| 2019). Foreachuser–itempair,wecomputecosine that the learned profiles capture meaningful pref- |
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erence–attributecorrespondencethatisdifficultto |
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e ·e |
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recoverfromindividualreviewsalone. |
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u i |
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Align(u,i) |
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∥e ∥∥e ∥ |
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u i |
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5 Conclusion |
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| wheree |
ande denotetheembeddingvectorsof |
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u i |
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In this |
paper, we |
propose |
DUET |
, a |
closed-loop |
| thegenerateduseranditemprofiles. |
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Highervalues |
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framework |
for |
jointly |
generating |
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and item |
| indicatestrongersemanticcompatibilitybetween |
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textualprofilesforrecommendation. |
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Unlikeprior |
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| modeleduserpreferencesanditemcharacteristics. |
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methods |
that rely |
on |
fixed templates |
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or indepen- |
| -------- |
--------------- |
--- |
---------- |
------ |
------- |
----------- |
--------- |
--------------- |
--- |
-------------- |
| Coverage |
(Faithfulness). |
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We measure |
token- |
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dently |
constructed |
profiles, |
DUET |
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treats profile |
| levelgroundingas: |
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generation |
as an |
exploration |
problem |
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and aligns |
| --- |
--- |
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---------- |
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------- |
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---------- |
| Tokens(profile)∩Tokens(history) |
representations directly with downstream recom- |
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Tokens(profile) |
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mendationperformance. |
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---- |
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(4) |
Specifically, |
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DUET |
integrates |
cue-based |
ini- |
| Thismetricquantifieshowmuchofthegenerated tialization, adaptive strategy construction, and |
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| profileissupportedbyhistoricaltextualevidence. feedback-drivenjointoptimizationtoproduceflex- |
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| We report coverage separately for user and item ible yet task-aligned user–item profiles. By opti- |
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| profiles. |
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mizing |
both profiles |
in |
a shared |
semantic |
space, |
| --------- |
--- |
--- |
--- |
--- |
------ |
------------- |
--- |
-------- |
-------- |
------ |
| AsshowninTable6,DUETachievesthehigh- theframeworkreducessemanticmismatchandcap- |
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| est semantic alignment across all datasets while turesinteraction-relevantsignalsthataredifficultto |
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Setting Yelp AmazonMusic AmazonBooks
MAE RMSE Acc F1 MAE RMSE Acc F1 MAE RMSE Acc F1
(%) (%) (%) (%) (%) (%)
DUETw/oRL 0.8283 1.3893 48.53 41.15 0.7322 1.1430 57.18 52.48 0.8741 1.4760 51.83 50.35
DUET(full) 0.5126 0.9485 61.23 55.18 0.3937 0.7564 67.96 63.89 0.4612 0.9089 64.38 59.27
Table5: EffectofRL-basedoptimization(usingQwen3(8B)).
User History:
Andre' S Grindle "Tour of U.S.A.", Rating: 5.0. If your looking for heavy metals dim orgins look no further.
Andre' S Grindle "Misfits", Rating:4.0. Actually the opening title track is more of a direct pop/rock type song. But the rock n roll
returns in a big way on "Hay Fever".
............
Item History:
Worlds Away, Rating: 4.0, Review: The CD is wonderful, and is a seminal piece of music for both the group and for those of us who
were 'of age' in the '70s and '80s. Pablo Cruise was - and is - a group that makes beautiful music like few others, and this
shipper/seller made it all the more special with a fast ship! THANKS!!!
Worlds Away, Rating: 5.0, I love this CD! I'd recommend the Pablo Cruise Millenium Collection to get to know this band. Worlds Away to
me represents the best of breezy '70s pop. Five stars!
............
USER PROFILE:
Andre Grindle is a dedicated music enthusiast with a deep appreciation for funk, soul, and progressive rock, particularly drawn to artists
who blend technical mastery with genre-defining innovation. His reviews consistently highlight albums that showcase musical complexity,
lyrical depth, and historical significance, often referencing artists like Stevie Wonder, Prince, and Rush. He values authenticity and
artistic vision, frequently praising albums that resist trend-chasing and prioritize creative integrity. His detailed, introspective reviews
suggest a preference for nuanced analysis over superficial praise, with a focus on emotional resonance, production quality, and the
artist's evolution. While he is generous with high ratings (often 5.0), he is also willing to critique perceived shortcomings, as seen in his
4.0 for "Test for Echo" and 3.0 for "The Color of Success." His engagement is characterized by a blend of personal anecdotes, technical
appreciation, and historical context, indicating a preference for music that transcends mere entertainment.
Item PROFILE:
"Worlds Away" by Pablo Cruise is celebrated as a peak-era funk-rock album that exemplifies the band's ability to merge polished
production with genre-blurring creativity. Users consistently praise its tight musicianship, genre-defying sound, and the band's ability to
craft catchy, emotionally resonant tracks. The album's appeal lies in its seamless fusion of rock, funk, and pop, with standout tracks like
"Love Will Find a Way" and "Worlds Away" receiving particular acclaim for their infectious melodies and lyrical depth. While some users
note its dated production compared to modern standards, the majority highlight its historical significance as a defining work of the
1970s rock-funk movement. The album resonates most with fans of 1970s progressive rock, genre-defying artists, and listeners seeking
well-crafted, emotionally engaging music. Its high average rating reflects widespread admiration for its artistic ambition and enduring
appeal, though a minority acknowledges its limitations in terms of modern production quality.
Figure4: Illustrationofthemutualcorrespondencebetweenuseranditem. Thehighlightedregionsdemonstrate
thatuserpreferencessummarizedintheuserprofilealignwiththekeyattributesextractedintheitemprofile,which
providescomplementaryinformationbeyondrawhistoriesandthusimprovespredictionaccuracy.
Method Yelp AmazonMusic AmazonBooks
Align UserCov. ItemCov. Align UserCov. ItemCov. Align UserCov. ItemCov.
10H 0.3902 /(1.00) /(1.00) 0.3577 /(1.00) /(1.00) 0.2604 /(1.00) /(1.00)
KAR(Xietal.,2024) 0.4932 0.1745 0.1823 0.4807 0.2170 0.2391 0.5702 0.1877 0.1544
RLMRec(Renetal.,2024) 0.4010 0.2646 0.3773 0.3931 0.3526 0.4889 0.4436 0.2994 0.3506
PALR(Yangetal.,2023) 0.4715 0.3675 0.1917 0.4938 0.2197 0.2390 0.5216 0.2050 0.2267
LG(Wangetal.,2025) 0.5709 0.2378 0.2682 0.5109 0.2546 0.3749 0.5506 0.3883 0.3364
R4Rec(Fangetal.,2025) 0.4882 0.2348 0.2330 0.4208 0.3328 0.3595 0.4946 0.2831 0.2949
Ours 0.6382 0.2880 0.3429 0.5947 0.4002 0.4482 0.7287 0.3457 0.3127
Table6: Semanticalignmentandcoverageofgeneratedprofiles. Cov. denotesthefractionoftokensgroundedin
historicaltext.
recoverfromrawhistoriesorstaticpromptsalone. reinforcementlearning–basedoptimization. These
resultssuggestthatadaptive,interaction-awaretex-
Experiments on multiple real-world datasets tualprofilesprovideapromisingdirectionformore
demonstratethatDUETconsistentlyoutperforms effective interpretable and performance-oriented
strongbaselinesunderdifferentbackbonemodels, recommendationsystems.
validating the effectiveness of joint profiling and
| Limitations |
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| ------------------------ |
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--- |
------------------- |
--- |
--- |
--- |
------------------------- |
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--- |
---------------- |
--- |
--- |
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Contentrecommen- |
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| the overall efficiency may be constrained when In2007Internationalconferenceonmachinelearn- |
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| --------------------------------- |
--- |
--- |
--- |
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---------- |
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------------ |
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| scalingtoverylargeuseroritemsets. |
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Second,the |
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| IEEE. |
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| quality |
of the generated |
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profiles |
is |
inherently |
de- |
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|
| ------- |
----------------- |
--- |
-------- |
--- |
------------- |
--- |
--------- |
------- |
--- |
----------- |
----- |
--- |
----- |
| pendent |
on the underlying |
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LLMs |
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and prompting |
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Yue Chen, |
Minghua |
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He, Fangkai |
Yang, |
Pu |
Zhao, |
| strategies. Variationsinmodelcapacityorprompt LuWang,YuKang,YifeiDong,YuefengZhan,Hao |
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Zhang. |
2025. |
Warriormath: |
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Enhancing |
the |
mathe- |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
------ |
----- |
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--- |
--------- |
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| ity,whichwedonotexplicitlycontrolinthecurrent |
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awareframework. |
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Preprint,arXiv:2508.01245. |
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| --- |
--- |
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-------------------------- |
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| recommendationscenarioswheresufficienthistor- |
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Paul Covington, |
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Jay |
Adams, and |
Emre |
Sargin. |
2016. |
| ------------ |
--- |
---------- |
--- |
----------------- |
--- |
--- |
--------------- |
--- |
--- |
---------- |
---- |
------- |
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| ical reviews |
are |
available. |
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The effectiveness |
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of |
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| Deepneuralnetworksforyoutuberecommendations. |
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| the proposed |
strategy |
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in domains |
with |
extremely |
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| ------------ |
-------- |
--- |
---------- |
---- |
--------- |
--- |
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--- |
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--- |
| InProceedingsofthe10thACMconferenceonrec- |
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| ommendersystems,pages191–198. |
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| remainstobefurtherexplored. |
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| DeepSeek-AI,DayaGuo,DejianYang,HaoweiZhang, |
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| JunxiaoSong,RuoyuZhang,RunxinXu,QihaoZhu, |
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|
hong |
Shao, |
Zhuoshu |
Li, Ziyi |
Gao, and |
181 |
others. |
| --- |
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2025. |
Deepseek-r1: |
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Incentivizing |
reasoning |
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capa- |
| Alldatasetsusedinthisworkarepubliclyreleased bilityinllmsviareinforcementlearning. Preprint, |
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| arXiv:2501.12948. |
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| bytheiroriginalowners,andwefollowtheofficial |
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| termsofuseassociatedwitheachdataset,utilizing |
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| JiaxinDeng,ShiyaoWang,KuoCai,LejianRen,Qigen |
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Hu, Weifeng |
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Ding, |
Qiang Luo, |
and |
Guorui |
Zhou. |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
----------- |
--- |
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---------- |
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------ |
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| bytheirrespectivelicenses;forreproducibility,the 2025. Onerec: Unifyingretrieveandrankwithgen- |
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| ment. Preprint,arXiv:2502.18965. |
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| anyrawuser-generatedcontentsuchasreviewtext AbhimanyuDubey,AbhinavJauhri,AbhinavPandey, |
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| aspartofthiswork. |
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Thisresearchdoesnotattempt |
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| ----------------- |
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-------------------------- |
--- |
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| AbhishekKadian,AhmadAl-Dahle,AieshaLetman, |
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| to identify or infer the identity of any individual Akhil Mathur, Alan Schelten, Amy Yang, Angela |
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Fan,and1others.2024. |
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Thellama3herdofmodels. |
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| --- |
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--- |
--- |
--- |
--- |
--- |
-------------------- |
--- |
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---------------------- |
--- |
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--- |
| user,andpriortotraininganddownstreamprofile |
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| arXivpreprintarXiv:2407.21783. |
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| generation, |
we |
apply text |
filtering |
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procedures |
to |
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| ----------- |
--- |
---------- |
--------- |
--- |
---------- |
--- |
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--- |
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--- |
| remove direct personal identifiers where applica- YiFang,WenjieWang,YangZhang,FengbinZhu,Qi- |
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Preprint, |
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--- |
-------------- |
--- |
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--- |
--- |
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--- |
--- |
--- |
--- |
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A ExperimentSetup
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| ---------------------- |
---------- |
-------------- |
---------- |
------ |
----------- |
--- |
------------ |
------ |
------ |
| arXiv:2409.13093. |
|
|
Dataset |
#Train |
#Valid |
|
#Test #User |
|
#Item |
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|
Music |
43,071 |
3,271 |
|
1,296 |
4,183 |
2,660 |
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Book |
71,972 |
6,144 |
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5,541 13,863 |
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13,515 |
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|
Yelp |
51,497 |
4,757 |
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4,328 |
8,453 |
13,426 |
|
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|
We conduct |
|
experiments |
|
on three |
widely |
used |
| real-worlddatasets: |
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• AmazonMusic(Music): |
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|
Thisreferstothe“Dig- |
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|
| --- |
--- |
--- |
--------------------- |
--- |
------ |
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-------------------- |
--- |
------ |
|
|
|
ital Music” |
|
subset |
of the |
well-known |
|
Amazon |
| Productdataset3,whichrecordsrichuserreviews, |
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| ratings,andtextualinformationaboutitems,such |
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as titles, |
across |
a |
broad |
range |
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cate- |
| --- |
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--- |
---------- |
------ |
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---------- |
----- |
| gories,ontheAmazonplatform.2 |
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• AmazonBook(Book): |
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Thisreferstothe“Book” |
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| --- |
--- |
--- |
------------------- |
--- |
--- |
--------------------- |
--- |
--- |
--- |
| subsetoftheAmazonProductdataset. |
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| dataset4, |
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• Yelp: |
This |
refers |
to the |
Yelp |
Open |
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| --- |
--- |
--- |
------- |
-------- |
-------------- |
-------- |
------- |
------------- |
--------- |
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which |
includes |
user |
reviews, |
ratings |
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for busi- |
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nesses |
such |
as restaurants |
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and |
retail shops, |
as |
| wellastextualinformationaboutthebusinesses. |
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| Itiswidelyusedinrecommendationtasks(Qiu |
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| etal.,2021). |
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| WeusetheentireMusicdatasetforexperiments, |
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while for |
the Book |
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and Yelp |
datasets, |
we |
utilize |
| --- |
--- |
--- |
------------------------------- |
-------- |
--- |
-------- |
--------- |
---------- |
------- |
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onlyasubsetduetotheirlargesize. |
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FortheBook |
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| fortheYelpdataset,weusedatafromthelastsix |
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| months. Foreachdataset,wesplititintotraining, |
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validation, |
and |
test |
sets based |
on |
the timestamps |
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| --- |
--- |
--- |
---------------- |
--- |
-------- |
---------- |
---- |
-------------- |
--- |
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of interactions, |
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ensuring |
that |
test |
interactions |
oc- |
| curafteralltrainingandvalidationinteractionsto |
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| preventinformationleakage(Jietal.,2023). |
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Regarding |
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data |
filtering, |
following |
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prior |
| --- |
--- |
--- |
--------- |
--- |
----------- |
---------- |
--------- |
-------- |
----- |
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work (Liu |
et |
al., 2019), |
we |
adopt |
a 5-core |
set- |
| tingtofilterthedataandexcludecold-startusers |
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and items—those |
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not |
appearing |
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in the |
training |
| --- |
--- |
--- |
--------------- |
--- |
---------- |
--------- |
---- |
------ |
-------- |
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|
set—from |
the |
validation |
and |
test |
sets. |
The sta- |
| tisticaldetailsoftheprocesseddatasetareprovided |
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| inTable7. |
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| 3https://cseweb.ucsd.edu/~jmcauley/datasets/ |
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| amazon/links.html. |
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| 4https://business.yelp.com/data/resources/ |
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| open-dataset/. |
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| A.1.1 |
ImplementationDetails |
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RLMRecPrompt |
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| Inourexperiments,weprimarilyemployQwen3- Role: Businessrecommendationassistant |
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| Task: Determinebusinesstypesauserislikely |
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| 8B(Team,2025)andLLaMA38BInstruct |
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(Dubey |
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| ------------------------------------------ |
--- |
----- |
--- |
--- |
------- |
---------- |
------------ |
--- |
--- |
--- |
| etal.,2024;Touvronetal.,2023)asboththerec- |
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toenjoy |
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| ommendation |
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model |
and |
the |
profile |
generation |
InputFormat: |
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| •Title: Businessname |
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| model. Thetrainingprocessisimplementedusing |
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| theTRL(vonWerraetal.). Keyhyperparameters, •Categories: Businesscategories |
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| suchasbatchsizeandlearningrate,aredetermined •Sentiment: Usersentimenttowardbusiness |
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| throughgridsearchtoachieveoptimalperformance. |
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| OutputRequirements: |
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| Moredetailscanbefoundinourcode. |
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- JSONformatonly
- Structure:
A.2 BaselinePrompts
{
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"summarization": |
"Types |
of businesses |
user |
| KARPrompt |
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| likelyenjoys"(≤100words), |
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| Task: Analyzeuserpreferencesbasedonbusi- |
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"reasoning": |
"Brief explanation |
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for summa- |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
------------ |
------------------ |
--- |
---------- |
| nessreviewinghistory |
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| rization"(nowordlimit) |
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| Input: {user_history}-User’sbusinessreview- |
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| } |
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| inghistorywithsentimentsovertime |
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- NoadditionaltextoutsideJSON
Instructions:
| 1. |
Analyze |
the |
user’s |
preferences |
considering |
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Input: |
INTERACTION |
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ITEMS: |
| businessnamesandcategories |
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| {user_history} |
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| 2. |
Take |
into account |
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sentiment |
patterns |
over |
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| --- |
---- |
------------ |
--- |
--------- |
-------- |
---- |
--- |
--- |
--- |
--- |
| time |
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| 3. |
Provideclearexplanationsbasedonreview- |
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LGPrompt |
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| --- |
-------------------------------------- |
--- |
--- |
--- |
--- |
--- |
-------- |
--- |
--- |
--- |
| inghistorydetails |
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| Youwillserveasanassistanttohelpmegener- |
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| 4. |
Consider |
other |
pertinent |
|
factors |
that may |
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| --- |
-------- |
----- |
--------- |
--- |
------- |
-------- |
--- |
--- |
--- |
--- |
| ateauserprofilebasedonthisuser’ssentiments |
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| influencepreferences |
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| historytobetterunderstandthisusers’interest |
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| andthuspredicthis/hersentimentaboutatarget |
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| PALRPrompt |
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item. Iwillprovideyouwithsomebehaviorhis- |
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| ---------- |
--- |
--- |
--- |
--- |
--- |
--- |
----------------------------------------- |
--- |
--------------- |
--- |
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toryoftheuserinthisformat: |
|
[itemattributes |
|
Task: Summarizeuserpreferencesusingkey-
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|
andsentiment]. |
Theuserprofileyougenerate |
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| words. |
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| shouldcontainasmuchusefulcontentaspossi- |
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| Input: |
|
{user_history} |
|
- historical |
businesses |
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|
|
| ------ |
--- |
-------------- |
--- |
------------ |
---------- |
--- |
--- |
--- |
--- |
--- |
| bletohelppredicttheuser’ssentimenttowards |
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| withusersentiments. |
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| anewbusiness. |
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| Output |
|
Format: |
An |
itemized |
list ranked |
by |
|
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|
| ------ |
--- |
------- |
--- |
-------- |
----------- |
--- |
--- |
--- |
--- |
--- |
| USERHISTORY:{user_history}. |
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| importance. |
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| PROFILEYOUGENERATE: |
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| Template: |
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• KEY_WORD_1: |
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"HIS- |
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| --- |
------------- |
--- |
--- |
--- |
--- |
----- |
--- |
--- |
--- |
--- |
| R4RecPrompt(Reasoner) |
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|
TORY_BUSINESS_1", |
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|
"HIS- |
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| --- |
----------------- |
--- |
--- |
--- |
--- |
----- |
--- |
--- |
--- |
--- |
| UserReviewHistory |
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| TORY_BUSINESS_2" |
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| ⟨H u organizedasbelow⟩ |
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|
• KEY_WORD_2: |
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|
"HIS- |
1. TitleofItem1 |
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| ------------- |
---------------- |
--- |
--- |
--- |
--- |
----- |
---------------- |
----------------------- |
----------- |
--- |
|
TORY_BUSINESS_3" |
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|
PositiveAspects: |
[Aspect1],[Aspect2],... |
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|
NegativeAspects: |
[Aspect1],[Aspect2],... |
|
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| Instructions: |
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|
User Preference |
Elements: |
[Preference |
1], |
| [Preference2],... |
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|
1. Extract |
key |
preference |
|
indicators |
from |
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| --- |
---------- |
--- |
---------- |
--- |
---------- |
---- |
--- |
--- |
--- |
--- |
| 2. TitleofItem2 |
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| userinteractionhistory. |
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|
PositiveAspects: |
[Aspect1],[Aspect2],... |
|
|
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
---------------- |
----------------------- |
--- |
--- |
| 2. Rankkeywordsbyimportance. NegativeAspects: [Aspect1],[Aspect2],... |
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|
User Preference |
Elements: |
[Preference |
1], |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
--------------- |
--------- |
----------- |
--- |
| [Preference2],... |
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|
cantlyworsethanDUETacrossalldatasets. |
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This |
| indicatesthatcoherentsummariesaloneareinsuf- |
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| ... |
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|
ficient, |
and that |
the gains |
of DUET |
|
arise from |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
-------- |
-------- |
--------- |
------- |
--- |
---------- |
| ItemReviewHistorybyOtherUsers |
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| ⟨H organizedinthesameformatasabove⟩ learnedsemanticabstractionratherthanextractive |
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| i |
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| compression. |
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| Task: |
Analyze |
whether |
|
the user |
will |
like the |
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|
|
| --------- |
------- |
------- |
--- |
---------- |
----------- |
-------- |
--- |
--- |
--- |
--- |
--- |
--- |
| new Music |
|
i based |
on |
the user’s |
preferences |
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| B.2 RankingunderHardNegativeSampling |
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| andtheitem’sfeatures. |
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Provideyourrationale |
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|
| --------------------- |
--- |
--- |
--- |
-------------------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| Toconstructamorechallengingrankingscenario, |
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| inoneconciseparagraph. |
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| wereplacerandomnegativeswithhardnegatives |
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generatedbyacollaborativefilteringmodel. |
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|
Specif- |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
---------------------------------------- |
--- |
--- |
--- |
--- |
------- |
| A.3 ExampleofCue |
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| ically,foreachuser,weretrievehigh-scoringitems |
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| Thefollowingexamplesillustratehowrawsignals |
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from an |
EASE(Steck, |
2019) |
model |
that |
the user |
| --------------------- |
--- |
--- |
---------------- |
--- |
--- |
---- |
------- |
----------- |
----- |
----- |
---- |
-------- |
| aredistilledintocues. |
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Asshownonthetop, |
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user |
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| hasnotinteractedwith,andcombinethemwiththe |
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| cuesemphasizehistoricalpreferences,whileitem |
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| ground-truthitemtoformthecandidateset. |
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| cues highlight metadata and user-group patterns. Table 9 reports the results. Compared to ran- |
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| Together, |
they |
provide |
minimal |
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but informative |
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| --------- |
---- |
------- |
------- |
--- |
--------------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| domsampling,performancedecreasesforallmeth- |
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| hypothesesforprofileexploration. |
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|
ods due |
to increased |
difficulty, |
while |
DUET |
con- |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
--------- |
------------ |
----------- |
---------------- |
---- |
------ |
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sistently |
maintains |
the |
best performance |
|
across |
| ExamplesofUserCues |
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datasets. |
Thisindicatesthattheimprovementsare |
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|
| ------- |
----- |
------ |
------ |
--- |
------- |
-------- |
--------- |
----------------------------------- |
--- |
--- |
--- |
--- |
| “enjoys |
retro |
puzzle |
games” |
— |
derived |
from re- |
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| robustandnotlimitedtotrivialrankingscenarios. |
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| peatedengagementwithclassictitles. |
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| “prefersconciseproductreviews”—inferredfrom B.3 RobustnessunderPreferenceDiversity |
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| apatternofshort,directcomments. |
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| WefurtheranalyzetherobustnessofDUETunder |
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| “tendstogivehighratingsbutrarelycomments”— |
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varyinglevelsofuserpreferencediversity. |
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Weuse |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
--------------------------------------- |
--- |
--- |
--- |
--- |
----- |
| highlightingconsistencybutlimitedfeedback. |
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| thevarianceofhistoricalratingsasaproxyforpref- |
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|
erencestability: |
|
lowvarianceindicatesconsistent |
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| --- |
--- |
--- |
--- |
--- |
--- |
--- |
---------------- |
--- |
------------------------------ |
--- |
--- |
--- |
| ExamplesofItemCues |
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|
preferences, |
while |
high |
variance |
corresponds |
to |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
------------ |
----- |
---- |
-------- |
----------- |
--- |
| “lightweighttrail-runningshoes”—derivedfrom |
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| diverseorpotentiallyconflictingsignals. |
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| productmetadata. |
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| Wepartitionusersintothreegroupsbasedonper- |
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| “popular |
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budget-conscious |
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users” |
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| centilethresholds(bottom33%,middle33%,top |
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| ferredfrompurchasepatterns. |
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| 33%ofratingvariance)andevaluateperformance |
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| “stylized |
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retro |
aesthetics” |
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| withineachgroup. |
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| itemdescriptions. |
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As shown |
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Table 10, |
performance |
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degrades |
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| smoothlyaspreferencediversityincreasesacross |
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all datasets. |
Importantly, |
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is grad- |
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| B AdditionalExperiments |
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| ualratherthancatastrophic,indicatingthatDUET |
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| B.1 Non-LLMBaselineviaExtractive |
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| remainsstableunderheterogeneousornoisyinter- |
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| Summarization |
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| actionhistories. |
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| To examine |
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whether |
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of DUET |
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| C TheUseofLargeLanguageModels |
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| from improved |
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semantic |
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representations |
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rather |
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| than generic |
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text generation, |
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we |
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| WeusedaLargeLanguageModel(LLM)onlyas |
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| LLMbaselinebasedonextractivesummarization. a writing assistant to polish the language of the |
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| Specifically, |
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we apply |
TextRank |
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(Mihalcea |
and |
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manuscript |
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| Tarau,2004)toselectsalientsentencesfromuser |
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justment,andclarityimprovement). |
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Theresearch |
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| historiesandconstructuserprofileswithoutusing ideas,methodologydesign,experiments,andanal- |
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| anygenerativemodel. ysiswereentirelyconceived,implemented,andval- |
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| The |
extracted |
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into the |
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| idatedbytheauthorswithoutrelianceontheLLM. |
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| samedownstreampredictorforratingestimation. |
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The LLM |
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ideation, |
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| Thisbaselineisolatestheeffectofreadablesumma- experimentaldesign,orresultinterpretation. |
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| rizationfromrepresentationlearning. |
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| Table8showsthatTextRankimprovesoversim- |
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| ple history |
truncation |
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(10H), |
but |
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signifi- |
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| ----------- |
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| Method |
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Yelp |
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AmazonMusic |
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AmazonBooks |
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MAE RMSE |
Acc |
F1(%) |
MAE |
RMSE |
Acc F1(%) |
MAE |
RMSE |
Acc F1(%) |
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(%) |
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(%) |
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(%) |
| 10H 0.8283 1.3893 48.53 41.15 0.7322 1.1430 57.18 52.48 0.8741 1.4760 51.83 50.35 |
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| TextRank 0.8104 1.1323 50.51 30.23 0.5914 0.8223 61.19 29.68 0.6328 0.8854 59.59 29.48 |
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| Ours 0.5126 0.9485 61.23 55.18 0.3937 0.7564 67.96 63.89 0.4612 0.9089 64.38 59.27 |
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| Table8: Comparisonwithanon-LLMextractivesummarizationbaseline(TextRank). |
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| Method |
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Yelp |
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| NDCG@1 NDCG@5 NDCG@10 NDCG@1 NDCG@5 NDCG@10 NDCG@1 NDCG@5 NDCG@10 |
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| 10H 0.1823 0.2815 0.4928 0.1875 0.3796 0.5153 0.1841 0.3146 0.4263 |
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| KAR(Xietal.,2024) 0.2156 0.3298 0.5412 0.3018 0.4896 0.6015 0.2965 0.4715 0.5834 |
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| RLMRec(Renetal.,2024) 0.2419 0.3472 0.5587 0.3371 0.5434 0.6162 0.2748 0.4526 0.5719 |
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| PALR(Yangetal.,2023) 0.2494 0.3563 0.5691 0.3395 0.5247 0.6115 0.2627 0.4634 0.5538 |
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| LG(Wangetal.,2025) 0.3187 0.4685 0.5814 0.4012 0.5674 0.6489 0.3795 0.5189 0.6284 |
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| R4Rec(Fangetal.,2025) 0.2575 0.3792 0.5526 0.2928 0.5912 0.6343 0.3013 0.4928 0.5959 |
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| Ours 0.3390 0.4873 0.6008 0.5123 0.6165 0.7025 0.4288 0.5638 0.6599 |
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Table9: |
RankingperformanceunderEASE-based(Steck,2019)hardnegatives. |
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| ------------- |
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| VarianceGroup |
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Yelp |
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| #Samp. MAE RMSE Acc F1 #Samp. MAE RMSE Acc F1 #Samp. MAE RMSE Acc F1 |
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(%) (%) |
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| Stable 1530 0.4017 0.8664 71.76 68.96 432 0.2368 0.5990 82.56 86.89 1876 0.3171 0.8403 76.64 72.56 |
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| Moderate 1354 0.4773 0.8309 60.09 46.26 436 0.3921 0.6628 64.16 47.02 1827 0.4495 0.8059 66.73 55.45 |
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| Diverse 1444 0.6632 1.1198 51.13 45.95 428 0.5536 0.9607 57.10 59.27 1838 0.6199 1.0603 49.52 46.35 |
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| Table10: PerformanceofDUETunderdifferentlevelsofuserpreferencediversity(measuredbyratingvariance). |
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| #Samp. denotesthenumberofsamplesineachgroup. |
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