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 | | | | User History: | Item History: | User History: | Item History: | | --- | --- | --- | -------------------- | ------------------------- | -------------------- | ------------------------- | | | | | • Misfits (Punk) | • Review: | • Misfits (Punk) | • Review: | | | | | • 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 6202 rpA 51 ]RI.sc[ 1v10831.4062:viXra • • Parliament Funk Bass" | usersanditemsasdensevectorsandlearnto | | | Parliament | Funk Bass" | | | | ------------------------------------- | --- | --- | ----------- | ------------------ | ------ | ------------------ | | | | | (Funk) | • Meta: "80s Rock" | (Funk) | • Meta: "80s Rock" | aligntheminasharedlatentspaceforrelevance | | | | | Duet | Independent | | | --- | --- | --- | --- | ----- | ------------ | --- | estimation. RecentLLM-basedrecommenders (Joint Alignment) Generation | instead leverage | natural-language | representa- | | | | | | ---------------- | ---------------- | ----------- | ------------- | ------------- | ------------- | ------------- | | | | | User Profile: | Item History: | User Profile: | Item History: | tionsthatareeasiertointerpretandintegrate Enthusiast of A peak-era FUNK- Fan of HEAVY A highly rated POP- | | | | FUNK and SOUL. | ROCK album with | METAL and | ROCK album with | | --- | --- | --- | --------------- | ---------------- | ---------- | ---------------- | withdownstreamreasoningmodules. Thispa- Values technical genre-blurring PUNK. Prefers polished | | | | mastery | creativity. | dark themes. | production. | | --- | --- | --- | ------- | ----------- | ------------ | ----------- | textual per studies how to construct effective Semantic Alignment ✘ Semantic Mismatch profilesforusersanditems,andhowtoalign themforrecommendation. Acentraldifficulty Figure1: DUETalignsrawuseranditemdatabytrans- | | | | forming them | into textual | profiles within | a shared se- | | --- | --- | --- | ------------ | ------------ | --------------- | ------------ | isthatthebestprofileformatisnotknownapri- manticspace. ori: manuallydesignedtemplatescanbebrittle | andmisalignedwithtaskobjectives. | | Moreover, | | | | | | -------------------------------- | --- | --------- | ------------ | ------------ | -------------------- | --- | | | | | to introduce | semantically | rich, human-readable | | generatinguseranditemprofilesindependently mayproducedescriptionsthatareindividually representationsforrecommendation(Wangetal., plausible yet semantically inconsistent for a 2025;Zhang,2024;Baoetal.,2023;Hongetal., | specificuser–itempair. | WeproposeDUET,an | | | | | | | ---------------------- | ---------------- | --- | -------------------------- | --- | ------------------- | --- | | | | | 2025a,b,c;Wangetal.,2024). | | Anaturaldirectionis | | interaction-awareprofilegeneratorthatjointly toreplacelatentvectorswithtextualuseranditem producesuseranditemprofilesconditionedon profilesthatcanbeinspected,edited,andreusedby | both user history | and item evidence. | DUET | | | | | | ----------------- | ------------------ | ---- | --- | --- | --- | --- | downstreamcomponentsincorporatedwithLLMs. | follows a three-stage | procedure: | it first turns | | | | | | --------------------- | ---------- | -------------- | --- | --- | --- | --- | However,existingLLM-basedapproachesremain rawhistoriesandmetadataintocompactcues, then expands these cues into paired profile limited in two important ways. First, directly promptsandthengenerateprofiles,andfinally promptinganLLMwithrawuseranditemhisto- optimizesthegenerationpolicywithreinforce- riestoobtainrecommendationsoftenyieldsnoisy mentlearningusingdownstreamrecommenda- andincompletesignals,especiallywhenhistories | tionperformanceasfeedback. | | Experimentson | | | | | | -------------------------- | --- | ------------- | --------- | ------------------------ | --- | ------------- | | | | | are long, | sparse, or heterogeneous | | (Wang et al., | threereal-worlddatasetsshowthatDUETcon- | | | | 2025). Second, | profile-based | methods | typically | | --- | --- | --- | -------------- | ------------- | ------- | --------- | sistentlyoutperformsstrongbaselines,demon- relyonmanuallydesignedtemplatesorhandcrafted stratingthebenefitsoftemplate-freeprofileex- attributes,whichrequiressubstantialhumanengi- plorationandjointuser–itemtextualalignment. Project code page: https://github.com/duet- neering and constrains the representation space. review/duet_code. More fundamentally, many approaches generate userprofilesanditemprofilesindependently,with- 1 Introduction | | | | out modeling | how user | preferences | and item se- | | --- | --- | --- | ------------ | -------- | ----------- | ------------ | Traditional recommendation systems represent manticsinteractatdecisiontime(Yangetal.,2023; Xietal.,2024). usersanditemsasdensevectorsandlearntoalign theminasharedlatentspaceforrelevanceestima- Toaddressthesechallenges,wepropose DUET, tion (Covington et al., 2016; Wu, 2023). While ajointuser–itemprofilegeneratorthattakesboth effective,suchembeddingsareopaque: theyoffer userhistoryanditemhistoryasinputandproduces limitedinterpretabilityandmakeitdifficulttoan- a paired set of profiles for the interaction. Cru- alyzewhyanitemisrecommended. Recentwork cially, DUET doesnotrequireprofiletemplates: it thereforeleverageslargelanguagemodels(LLMs) istrainedwithreinforcementlearningusingfeed- 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 | | | | | Item | DU | | | Duet | | | | | ----------- | --- | --- | --- | ---- | --- | --- | --- | ---- | --- | --- | --- | | ItemHistory | | | | | ET | | | | | | | Initial CUE Rated: Baba Is You (5★) User: “Prefers retro puzzle games” Reviewed: “I love logic- Item: “Retro-style indie puzzle game” | | | | based puzzle games.” | | | Profile Prompt | | | | | | | --- | --- | --- | -------------------- | --- | --- | -------------- | --- | --- | --- | --- | --- | Rated: The Witness (5★) User: “Describe the user's typical | | | | Rated: Fez (4★) | | puzzle-game preferences … | | | 𝑃𝑟𝑜𝑓𝑖𝑙𝑒1 𝑃𝑟𝑜𝑓𝑖𝑙𝑒2 | … | 𝑃𝑟𝑜𝑓𝑖𝑙𝑒𝑛 | | | --- | --- | --- | --------------- | --- | ------------------------- | --- | --- | ----------------- | --- | -------- | --- | Item: “… item's core mechanics… | | | | | | | | Profile | Downstream Task Env | | | | | ----------- | --- | --- | ----------------------- | ---- | --------------------------------- | --- | ------- | --------------------- | --- | --- | --- | | | | | | User | User: “A player who enjoys logic- | | | | | | | | UserHistory | | | | | intensive indie puzzle games, | | | | | | | | | | | Category: Indie Puzzle | | | | | Ranking correctness | | | | prefers retro visual aesthetics…” | | | | Game | | | | | &format Reward | | | | | --- | --- | --- | ---- | --- | --- | --- | --- | -------------- | --- | --- | --- | Reviews: Item: “A retro-style indie puzzle | | | | “Challenging logic | | game featuring challenging | | | Group Computation | | | | | --- | --- | --- | -------------------- | ------------------------ | ------------------------------- | --- | --- | ------------------- | --- | ---- | --- | | | | | mechanics.” | | logic mechanics that appeal to | | | | | | | | | | | “ R e t r o p ix | e l- a rt st y l e . ” | hardcore puzzle players.” | | | 𝐴𝑑𝑣1 𝐴𝑑𝑣2 | | | | | | | | “ H a r d b u | t re w a r d i n g . ” | | | | | … | 𝐴𝑑𝑣𝑛 | | | | | | | Figure2: | OverviewoftheDUETframework. | | | | | | | ration via Adaptive Profile Prompt Discovery 3.2 Cue-BasedInitialization | jointly | explores | user-item’s | | profile prompts | that | | | | | | | | ---------- | -------- | ----------- | -------- | --------------- | -------- | -------- | --------- | -------- | -------- | --- | --------- | | | | | | | | Raw user | histories | and item | metadata | | are often | | define how | user | and item | profiles | should | be writ- | | | | | | | noisy,redundant,andnotdirectlysuitableforpro- ten. (3)OptimizationviaOn-policyExploration | | | | | | | fileconstruction. | | Toaddressthis,DUETintroduces | | | | | --- | --- | --- | --- | --- | --- | ----------------- | --- | ---------------------------- | --- | --- | --- | jointlyoptimizesuseranditemprofilesunderdown- theconceptofcues: concisehypothesesthatsum- streamrecommendationfeedback. | | | | | | | marize minimal | | but informative | | aspects | of users | | ------------------- | ------ | --- | ------------------------- | ------- | -------- | -------------- | ------------ | --------------- | -------------- | ------- | -------- | | All three | stages | are | realized | through | a single | | | | | | | | | | | | | | and items. | These | cues act | as lightweight | | seeds, | | passinputandoutput: | | | cueextraction,self-prompt | | | | | | | | | | | | | | | | which are | deliberately | underspecified | | so | that the | construction,andprofilegenerationareproduced systemcansubsequentlyexplorericherprofilefor- inasinglesequence-to-sequencegenerationpass mats. atinferencetime,enablingefficientdeployment. | | | | | | | Definition1(Cue). | | Acueisaminimaltextualhy- | | | | | --- | --- | --- | --- | --- | --- | ----------------- | --- | ------------------------ | --- | --- | --- | 3.1 ProblemFormulation pothesisderivedfromhistoricaldatathathighlights WeformulateprofilegenerationinDUETasanon- one potential aspect of a user’s preference or an policyreinforcementlearningproblem,motivated item’scharacteristic. Ratherthanaimingforcom- bytheabsenceofanytextualgroundtruthdefining pleteness, cues capture partial but salient signals an optimal user or item profile. Profile quality is thatserveasstartingpointsforprofileexploration. evaluatedsolelybyitsfunctionalutilityinafixed | | | | | | | To extract | cues | automatically, | | the | LLM is | | --- | --- | --- | --- | --- | --- | ---------- | ---- | -------------- | --- | --- | ------ | recommendationenvironment. | | | | | | | prompted | to summarize | minimal | | but informative | | | ---- | ---------- | --- | ------------ | ------ | ------- | ------------------------ | ------------ | ------- | ----------------- | --------------- | --- | | DUET | is modeled | as | a generative | policy | π θ in- | | | | | | | | | | | | | | aspectsofuseroritemdata. | | | Forexample,givena | | | teractingwithafrozendownstreamrecommender. user’sinteractionhistory,themodelisguidedwith | For each | user–item | pair, | the | state is | defined as | | | | | | | | -------- | --------- | ----- | --- | -------- | ---------- | --- | --- | --- | --- | --- | --- | instructionssuchas: | s = {H | ,H },whereH | | andH | denotetheuser | | | | | | | | | ------ | ----------- | --- | ---- | ------------- | --- | --- | --- | --- | --- | --- | --- | | | u i | | u | i | | | | | | | | and item interaction histories. An action corre- CueExtractionPrompt spondstoasingle-passjointgeneration “Fromthehistorybelow,analyzetheuser’s a = {C u ,S u ,P u ,C i ,S i ,P i }, historicalinteractionstounderstandprefer- ences,ratingbehavior,reviewsentimentor | where | (C ,C | ) are | cues | distilled | from his- | | | | | | | | ----- | ----- | ----- | ---- | --------- | --------- | --- | --- | --- | --- | --- | --- | u i | | | | | | | anyotherdimension. | | Keepthedescription | | | | | -------- | ------------ | ----------- | --- | ----------- | ------- | ------------------ | --- | ------------------ | --- | --- | --- | | tory, (S | u ,S i ) are | constructed | | user-item’s | profile | | | | | | | prompts,and(P ,P )arethefinaltextualprofiles. conciseandavoidfullsentences.” u i | Thepolicyπ | | (a | s)definesajointdistribution | | | | | | | | | | | ---------- | --- | -------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | θ over the entire generation sequence. As an on- This lightweight guidance allows the LLM to policy agent, DUET is optimized using rewards maprawhistoriesandmetadataintocompacttex- from its own sampled generations rather than by tualcues. Thedetaildexampleofcuecanbefound | imitatingfixedsummaries. | | | | | | inAppendixA.3. | | | | | | | ------------------------ | --- | --- | --- | --- | --- | -------------- | --- | --- | --- | --- | --- | | 3.3 | JointExplorationviaAdaptiveProfile | | | | | | Single-PassInput | | | | | | | --- | ---------------------------------- | --- | --- | --- | --- | --- | ---------------- | --- | --- | --- | --- | --- | PromptDiscovery Task:Generatestructuredprofilesfortheuseranditemfrom historicaldata. Aftercueextraction,DUETdoesnotdirectlysum- Combined Context: [User History H u ] + [Item | | | | | | | | History | H i ]+[Avg | Ratings] | | | | | ------------------------------------ | --- | --- | --- | --- | --- | ---------- | ------- | ---------- | -------- | --- | --- | --- | | marizeuserinterestsoritemattributes. | | | | | | Instead,it | | | | | | | “First,extractaconciseCueforbothuseranditem.Second, | explores | | the space | of profile | construction | | strate- | | | | | | | | -------- | --- | --------- | ---------- | ------------ | --- | ------- | --- | --- | --- | --- | --- | --- | basedonthecues,constructaProfilePromptthatdefines gies—natural-languagepromptsthatdefinethefor- thedescriptionlogic.Finally,generatetheProfileguidedby theconstructedProfilePrompt.” mat,abstractionlevel,andattributeselectionlogic ↓DUETForwardPass↓ | usedtogenerateprofiles. | | | | Explorationistherefore | | | | | | | | | | -------------------------------------------- | --- | --- | --- | ---------------------- | --- | --- | ----------------- | --- | --- | --- | --- | --- | | performedoverhowprofilesshouldbeconstructed, | | | | | | | Single-PassOutput | | | | | | User ratherthanoversuperficialtextualparaphrases. Cue:“prefersnostalgiclogic-basedchallenges” For each user–item pair, DUET intro- Profileprompt: “Focuson1990svisualaestheticsand | duces | an | explicit | intermediate | | variable, | the | strategicdepth.” | | | | | | | ----- | --- | -------- | ------------ | --- | --------- | --- | ---------------- | --------- | --- | ----- | ----------- | ------------ | | | | | | | | | Profile: | “A player | who | seeks | retro-style | visual charm | constructed_prompt,whichservesasadiscrete pairedwithdeepstrategicreasoning.” | and | interpretable | | profile | prompt | S. | This profile | | | | | | | | --- | ------------- | --- | ------- | ------ | --- | ------------ | --- | --- | --- | --- | --- | --- | Item promptisashortnatural-languageinstruction(e.g., Cue:“retro-styleindiepuzzlewithhighdifficulty” “Describetheuser’stypicalgamingpreferencesand | | | | | | | | Profile | prompt: | “Describe | | pixel-art | graphics and | | --- | --- | --- | --- | --- | --- | --- | ------- | ------- | --------- | --- | --------- | ------------ | engagementpatterns”)thatspecifieswhataspects intellectualdifficultytomatchlogicpreference.” Profile:“A2Dexperiencefeaturingpixelatednostalgia | to | describe | and | how to | organize | them, | without | | | | | | | | --- | -------- | --- | ------ | -------- | ----- | ------- | --- | --- | --- | --- | --- | --- | andchallengingmechanicsthatdemandlogicaldeduction.” | containingtheprofilecontentitself. | | | | | | Conditioned | | | | | | | | ---------------------------------- | --- | --- | --- | --- | --- | ----------- | --- | --- | --- | --- | --- | --- | onS,themodelgeneratesthefinaluseranditem Figure3: Single-passgenerationinDUET: cueextrac- | profiles. | | | | | | | tion,profileprompt(constructedprompt),andprofile | | | | | | | --------- | --- | --- | --- | --- | --- | --- | ------------------------------------------------ | --- | --- | --- | --- | --- | generationareproducedinonepassforbothuserand | Exploration | | | is driven | by treating | | the profile | | | | | | | | ----------- | --- | --- | --------- | ----------- | --- | ----------- | --- | --- | --- | --- | --- | --- | item. | promptS | | asastochasticactionsampledfromthe | | | | | | | | | | | | ------- | --- | --------------------------------- | --------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | policyπ | | (S | Cue). | Duringtraining,theagentsam- | | | | | | | | | | θ plesdifferentprofilepromptinstructions,executes | | | | | | | | andintellectualdifficulty”tomatch. | | | | | RLreinforces | | ---------------------------- | --- | --- | --- | --- | ---------------- | --- | ---------------------------------- | --- | --- | --- | --- | ------------ | | themtogenerateprofilepairs(P | | | | | ,P ),andreceives | | | | | | | | u i thissharedsemanticdirection,suppressingirrele- | a reward | | based | on downstream | | recommendation | | | | | | | | | --------- | --- | ----------------------------------- | ------------- | --- | -------------- | --- | ------------ | ------------- | -------- | --- | ----- | ---------------- | | | | | | | | | vant signals | and | forcing | the | final | profiles to con- | | accuracy. | | Thepolicyisoptimizedtoreinforcepro- | | | | | | | | | | | | | | | | | | | verge | into a shared | semantic | | space | of nostalgia | filepromptsthatconsistentlyyieldhigherrewards, andlogic,significantlyimprovingrecommendation enablingthemodeltoactivelysearchforeffective accuracy. profileconstructionformats. Thisprocessisrealizedasaunifiedgeneration 3.4 OptimizationviaOn-policyExploration pass, | | | | | | | | On-policy | optimization. | | | We train | the profile | | --- | --- | --- | --- | --- | --- | --- | --------- | ------------- | --- | --- | -------- | ----------- | O = [ Cue → profilepromptS → Profile], generator π in an on-policy manner against a | | (cid:124)(cid:123)(cid:122)(cid:125) | | | | | (cid:124) (cid:123)(cid:122) (cid:125) | | θ | | | | | | --- | ------------------------------------ | --- | --------- | ------------------ | --------- | -------------------------------------- | --- | --- | --- | --- | --- | --- | | | | | (cid:124) | (cid:123)(cid:122) | (cid:125) | | | | | | | | Context ProfileConstructionPrompt Execution frozendownstreammodelf, whichservesasthe | | | | | | | (1) | environment | critic. | For | each | sampled | user–item | | --- | --- | --- | --- | --- | --- | --- | ----------- | ------- | --- | ---- | ------- | --------- | wheretheprofilepromptS parameterizesthepro- pair (u,i), the policy generates (P ,P ) and re- u i filegenerator. Atinferencetime,DUETcollapses ceivesascalarrewardbasedonthepredictionac- thelearnedprofilepromptdistributionandgreed- curacy of f(P ,P ). The policy parameters are u i ilyexecutestheoptimalprofilepromptinasingle updatedtoreinforcegenerationsthatleadtolower | forwardpass,introducingnoadditionallatency. | | | | | | | predictionerror. | | | | | | | ------------------------------------------- | --- | ---------- | ------ | ------------ | -------- | ------- | --------------------------- | --- | --- | --- | --- | ------------- | | Example | | of profile | prompt | Exploration. | | As | | | | | | | | | | | | | | | Continuousfractionalreward. | | | | | Usingdiscrete | | illustrated | | in Figure | 3, | DUET | resolves | this by | | | | | | | integerratingsasrewardsleadstosparseandunsta- | identifying | | a shared | context | in | a single | forward | | | | | | | | ----------- | --- | -------- | ------- | --- | -------- | ------- | --- | --- | --- | --- | --- | --- | blefeedback,asnear-misspredictions(e.g.,predict- pass. ThepolicyextractsacoordinatedCue(e.g., ing4foragroundtruth5)receivethesamepenalty “prefersretropuzzlegames”)andconstructsapro- | | | | | | | | assevereerrors. | | Toprovidedenseandinformative | | | | | ---- | ------ | ---- | ------------------- | --- | --- | -------- | --------------- | --- | ---------------------------- | --- | --- | --- | | file | prompt | (the | constructed_prompt) | | | that in- | | | | | | | feedback,wedefineacontinuousfractionalreward: structsthemodeltohighlight“nostalgicaesthetics | and | strategic | logic” | for | the user, | while | simulta- | | | | | | | | ------- | ---------- | ------ | ---------- | ---------- | ----- | -------- | --- | ------- | --- | ---- | ------ | -------- | | | | | | | | | | | | | |y −yˆ | | | | | | | | | | | | R (u,i) | | = 1− | ui | ui , (2) | | neously | describing | | the item’s | “pixel-art | | graphics | | perf | | | | | M 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- | | | | ranking. | | | | | | -------------------------------------------- | --- | --- | --- | --------- | ---------- | ---------- | ---- | ------- | | | | | | Baselines | We compare | our method | with | several | inducedrepresentationcollapse. | | | | | representative | baselines. | 10H | directly | uses the | | --- | --- | --- | --- | -------------- | ---------- | --- | -------- | -------- | 4 Experiment mostrecentinteractionhistoriesforpredictionwith- | | | | | outconstructingexplicitprofiles. | | | KAR(Xietal., | | | --- | --- | --- | --- | -------------------------------- | --- | --- | ------------ | --- | 4.1 ExperimentalSettings 2024)augmentsrecommendationmodelswithex- Datasets. Experiments were conducted on three ternalreasoningknowledgeaboutuserpreferences widelyusedreal-worlddatasets. AmazonMusic andfactualknowledgeaboutitemsextractedfrom (Music) and Amazon Book (Book) are derived LLMs,whicharetransformedintotask-compatible fromtheAmazonProductdataset1,whileYelpis representations. RLMRec(Renetal.,2024)lever- fromtheYelpOpendataset2. Alldatasetsinclude agesLLMstolearnsemanticuseranditemrepre- userreviews,ratings,andrichtextualinformation. sentationsfromtextualsignalsandalignsthemwith WeusedthefullAmazonMusicdataset,butonly collaborativerelationalinformationthroughcross- subsets (latest two months for Book and six for viewrepresentationlearning. PALR(Yangetal., Yelp). Data was split by timestamp into training, 2023)fine-tunesalargelanguagemodelasarank- validation,andtestsetstopreventinformationleak- ing component that selects preferred items from age(Jietal.,2023). retrievedcandidatesexpressedinnaturallanguage. EvaluationMetrics LG (LettinGo) (Wang et al., 2025) explores di- For each observed user–item interaction (i.e., verseuserprofilecandidateswithLLMsandaligns a review record), we construct the evaluation profilegenerationwithdownstreamrecommenda- instance based on the user’s interaction history tionperformanceviapreferenceoptimization. Rea- strictly prior to the corresponding timestamp. son4Rec(Fangetal.,2025)introducesadelibera- tiverecommendationframeworkthatincorporates Specifically,theuser’srecenthistoricalinteractions beforethecurrentinteractionareusedtogenerate explicitstep-wisereasoningoveruserpreferences auserprofile,whilehistoricalreviewsfromother toguideratingprediction. | users are used | to generate | an item profile. | The | | | | | | | -------------- | ----------- | ---------------- | --- | --- | --- | --- | --- | --- | 4.2 MainResults downstreamrecommendationsystemthenpredicts theratingofthetargetitemconditionedontwoin- Table 1 presents a comparison of our proposed puts: thegenerateduserprofile,andthegenerated method against five baselines on three datasets: | | | | | Amazon | Music, Amazon | Books, | and | Yelp. We | | ------------ | ------------------------------- | --- | --- | ------ | ------------- | ------ | --- | -------- | | itemprofile. | Anexceptionisthe10Hbaseline,for | | | | | | | | which the downstream model directly consumes use Qwen3-8B (Team, 2025) and LLaMA3-8B therawrecentinteractionhistories. (Dubey et al., 2024) as both the profile generator andthedownstreamrecommendationmodel,with | We evaluate | performance | using four | widely | | | | | | | --------------- | ----------- | ------------------ | ------- | ------------ | ----------- | ----- | ----------- | ------- | | | | | | a prediction | temperature | of 0. | In addition | to rat- | | adopted metrics | (Wang | et al., 2025; Fang | et al., | | | | | | 2025): MeanAbsoluteError(MAE),RootMean ingpredictionresults,wealsoreportranking-based SquareError(RMSE),Accuracy,andF1score. evaluation results in Table ??, which assess the effectivenessofthelearnedprofilesfromadown- Inadditiontoratingprediction,wefurtherevaluate thegenerateduseranditemprofilesunderarank- streamrankingperspective. | | | | | Overallsuperiorityoverstrongbaselines. | | | | As | | --- | --- | --- | --- | -------------------------------------- | --- | --- | --- | --- | 1https://cseweb.ucsd.edu/~jmcauley/datasets/ shown in Table 1, our method consistently out- amazon/links.html | | | | | performs | the strongest | baselines | across | all three | | --- | --- | --- | --- | -------- | ------------- | --------- | ------ | --------- | 2https://business.yelp.com/data/resources/ | open-dataset/ | | | | datasetsunderbothQwen3-8BandLLaMA3-8B. | | | | | | ------------- | --- | --- | --- | -------------------------------------- | --- | --- | --- | --- | 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 | | | | Yelp | | | AmazonMusic | | | AmazonBooks | | | ------ | --- | --- | --- | ---- | --- | ------ | ----------- | --- | --- | ----------- | ------ | | | | | 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 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 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 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 etal.,2025)) 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 JointOpt.(Ours) Table3: AblationstudyondifferentdesignconfigurationsinDUETusingQwen3(8B). | Method | | Yelp | | | | AmazonMusic | | | | AmazonBooks | | | ------ | -------- | ---- | --- | ----- | --- | ----------- | --- | ----- | --- | ----------- | --------- | | | MAE RMSE | | Acc | F1(%) | MAE | RMSE | Acc | F1(%) | MAE | RMSE | Acc F1(%) | | | | | (%) | | | | (%) | | | | (%) | 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 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 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 Table4: Impactofhistoricalinteractionlengthonprofilequality(usingQwen3(8B)). tionyieldssubstantialperformanceimprovements jointoptimizationarecombined. Underthisfull across all datasets. For example, on Yelp, MAE configuration, accuracy reaches 61.23% on Yelp, isreducedfrom1.1235to0.7218andaccuracyin- 67.96%onAmazonMusic,and64.38%onAma- creases from 23.17% to 55.48%, confirming that zon Books, with corresponding MAE values of textualprofilesprovidesignificantlymoreinforma- 0.5126, 0.3937, and 0.4612, respectively. Com- tiverepresentationsthanrawinteractionhistories. pared with the LettinGo-style configuration, this | | | | | | | setting | yields | consistent | | improvements | across all | | ------ | ---------------- | --- | ----- | --- | ------ | --------- | ------ | -------------------------------------- | --- | ------------ | ---------- | | Adding | the cue&strategy | | layer | on | top of | pro- | | | | | | | | | | | | | datasets. | | Overall,theablationresultsindicatethat | | | | filegenerationleadstomodestbutconsistentgains. whileprofilegenerationandjointoptimization OnAmazonMusic,MAEfurtherdecreasesfrom 0.6597to0.5708,whileaccuracyslightlyimproves contributesubstantiallytoperformancegains,inte- gratingcue&strategydiscoveryfurtherenhances | from58.67%to58.91%. | | | Althoughthenumerical | | | | | | | | | | ------------------- | --- | --- | -------------------- | --- | --- | --- | ------------- | --- | -------- | --------- | ------------ | | | | | | | | the | effectiveness | | of joint | user–item | optimization | improvementsintroducedbystrategyalonearelim- ited, these results suggest that strategy discovery byprovidingastructuredspaceforexplorationand refinement. primarilyreshapeshowpreferenceinformationis abstractedandexpressed,ratherthandirectlyopti- Impactofhistoricalinteractionlengthonprofile mizingpredictionaccuracyinisolation. | | | | | | | quality. | | Table | 4 analyzes | the | impact of histori- | | --- | --- | --- | --- | --- | --- | -------- | --- | ----- | ---------- | --- | ------------------ | Whenenablingjointoptimizationwithoutstrat- cal interaction length on profile quality. Across thethreedatasets,varyingthenumberofhistorical | egy (i.e., | the LettinGo | (Wang | et | al., 2025)-style | | | | | | | | | ---------- | ------------ | ----- | --- | ---------------- | --- | --- | --- | --- | --- | --- | --- | configuration),performanceimprovesmorenotice- interactions leads to only moderate performance ablyacrossdatasets. OnAmazonMusic,accuracy differences, indicating that our method does not | | | | | | | strongly | | depend | on long | histories. | In particular, | | --- | --- | --- | --- | --- | --- | -------- | --- | ------ | ------- | ---------- | -------------- | increasesto62.37%,comparedto58.67%withpro- filegenerationalone. Inourimplementation,this usingamoderatehistorylength(e.g.,30–50inter- settingcorrespondstoareproductionofLettinGo, actions)alreadyachievescompetitiveorbestper- whereprofilegenerationandjointoptimizationare formanceonmostmetrics,whilefurtherincreasing thehistorylengthprovideslimitedadditionalgains. | appliedwithoutthestrategylayer. | | | | Whiletheorig- | | | | | | | | | ------------------------------- | ------- | --------- | --- | ------------- | ---------- | --- | --- | --- | --- | --- | --- | | inal method | focuses | primarily | on | user | profiling, | | | | | | | OnYelpandAmazonBooks,extendingthehis- weextendtheoptimizationtobothuseranditem torylengthbeyondthisrangedoesnotconsistently profilestoensureafaircomparison. improveaccuracyorF1scoreandmayslightlyde- Thestrongestandmostconsistentperformance gradeperformance,suggestingthatexcessivehis- is achieved when cue&strategy discovery and toricalinteractionscanintroducenoisyorlessrele- 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. | | | | | contrast,DUETmaintainsbothhighalignmentand | | | | | | | ------------ | --- | --- | --- | --- | ------------------------------------------ | --- | --- | --- | --- | --- | Without RL, the generator reduces to a static mid-to-highcoverage,suggestingamoreeffective mapping from interaction history to textual pro- balancebetweensemanticabstractionandevidence | files,lackingadaptiveselectionofrelevantsignals. | | | | | preservation. | | | | | | | ------------------------------------------------ | --- | --- | --- | --- | ------------- | --- | --- | --- | --- | --- | Incontrast,RLenablesoptimizingprofileconstruc- 4.5 CaseStudy tionunderrewardfeedback,resultinginmoredis- Figure4presentsarepresentativecasestudythat | criminative | representations.These | | results | demon- | | | | | | | | ----------- | --------------------- | --- | ------- | ------ | --- | --- | --- | --- | --- | --- | strate that RL-based optimization is essential for demonstrates how semantically aligned user and | DUET | . | | | | itemprofiles | enableaccurate | | prediction | | thattran- | | ---- | --- | --- | --- | --- | ------------ | -------------- | --- | ---------- | --- | --------- | scendsthelimitationsofsparserawinteractionhis- 4.4 SemanticAnalysisofGeneratedProfiles tories. In the user profile, initially scattered and To better understand the source of performance fragmentedpreferencecuesaresystematicallydis- gains, we analyze whether the generated profiles tilled into a stable and coherent preference struc- exhibitmeaningfulsemanticpropertiesratherthan turefavoringfunk,soul,andprogressiverock(or- servingasintermediatetextualartifacts. Weintro- ange), coupled with a pronounced emphasis on duce two complementary metrics to characterize musicalcomplexityandhistoricalsignificance(pur- semanticcompatibilityandgrounding. ple). Thecorrespondingitemprofileexhibitsstrik- Semantic Alignment. We measure the ingsymmetry,characterizingthealbumasanexem- embedding-levelsimilaritybetweengenerateduser plaryfunk-rockcomposition(orange)andposition- anditemprofilesusingall-mpnet-base-v2from ingitasadefiningandculturallyinfluentialrelease Sentence-Transformers (Reimers and Gurevych, of the 1970s (purple). This example shows 519 2019). Foreachuser–itempair,wecomputecosine that the learned profiles capture meaningful pref- | similarity: | | | | | erence–attributecorrespondencethatisdifficultto | | | | | | | -------------------------------- | -------------------------------- | --- | ------------ | --- | ----------------------------------------------- | --------- | ------- | ---------- | ---- | ----------- | | | | | e ·e | | recoverfromindividualreviewsalone. | | | | | | | | | | u i | | | | | | | | | | Align(u,i) | = | | (3) | | | | | | | | | | | ∥e ∥∥e ∥ | | | | | | | | | | | | u i | | 5 Conclusion | | | | | | | wheree | ande denotetheembeddingvectorsof | | | | | | | | | | | | u i | | | | In this | paper, we | propose | DUET | , a | closed-loop | | thegenerateduseranditemprofiles. | | | Highervalues | | | | | | | | | | | | | | framework | for | jointly | generating | user | and item | indicatestrongersemanticcompatibilitybetween | | | | | | textualprofilesforrecommendation. | | | | | Unlikeprior | | --- | --- | --- | --- | --- | --------------------------------- | --- | --- | --- | --- | ----------- | modeleduserpreferencesanditemcharacteristics. | | | | | | methods | that rely | on | fixed templates | | or indepen- | | -------- | --------------- | --- | ---------- | ------ | ------- | ----------- | --------- | --------------- | --- | -------------- | | Coverage | (Faithfulness). | | We measure | token- | | | | | | | | | | | | | dently | constructed | profiles, | DUET | | treats profile | levelgroundingas: | | | | | | generation | as an | exploration | problem | | and aligns | | --- | --- | --- | --- | --- | ---------- | ----- | ----------- | ------- | --- | ---------- | |Tokens(profile)∩Tokens(history)| representations directly with downstream recom- Cov = | | | |Tokens(profile)| | | | mendationperformance. | | | | | | | --- | --- | ----------------- | --- | --- | --------------------- | --- | ---- | ---------- | --------- | ---- | | | | | | (4) | Specifically, | | DUET | integrates | cue-based | ini- | Thismetricquantifieshowmuchofthegenerated tialization, adaptive strategy construction, and profileissupportedbyhistoricaltextualevidence. feedback-drivenjointoptimizationtoproduceflex- We report coverage separately for user and item ible yet task-aligned user–item profiles. By opti- | profiles. | | | | | mizing | both profiles | in | a shared | semantic | space, | | --------- | --- | --- | --- | --- | ------ | ------------- | --- | -------- | -------- | ------ | AsshowninTable6,DUETachievesthehigh- theframeworkreducessemanticmismatchandcap- est semantic alignment across all datasets while turesinteraction-relevantsignalsthataredifficultto 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 | | | | | | | References | | | | | | | | ----------- | --- | --- | --- | --- | --- | --- | ---------- | ----- | ------ | ---- | ------ | ------ | ----- | | | | | | | | | Keqin Bao, | Jizhi | Zhang, | Yang | Zhang, | Wenjie | Wang, | Despiteitseffectiveness,ourapproachhasseveral FuliFeng,andXiangnanHe.2023. Tallrec: Anef- fectiveandefficienttuningframeworktoalignlarge | limitations. | First, | the | proposed | framework | | relies | | | | | | | | | ------------ | ------ | --- | -------- | --------- | --- | ------ | -------------------------------- | --- | --- | --- | --- | ---------- | --- | | | | | | | | | languagemodelwithrecommendation. | | | | | InProceed- | | onlargelanguagemodelsforbothprofilegenera- ingsofthe17thACMConferenceonRecommender tion and downstream recommendation, which in- Systems,pages1007–1014. | troduces | additional | computational | | | overhead | dur- | | | | | | | | | -------- | ---------- | ------------- | --- | --- | -------- | ---- | --- | --- | --- | --- | --- | --- | --- | TingChen,Wei-LiHan,Hai-DongWang,Yi-XunZhou, | ingtrainingandinference. | | | Whileourexperiments | | | | | | | | | | | | ------------------------ | --- | --- | ------------------- | --- | --- | --- | ------------------------- | --- | --- | --- | ---------------- | --- | --- | | | | | | | | | BinXu,andBin-YuZang.2007. | | | | Contentrecommen- | | | showconsistentgainsacrossdifferentbackbones, dationsystembasedonprivatedynamicuserprofile. the overall efficiency may be constrained when In2007Internationalconferenceonmachinelearn- | | | | | | | | ing and | cybernetics, | | volume | 4, pages | 2112–2118. | | | --------------------------------- | --- | --- | --- | --- | ---------- | --- | ------- | ------------ | --- | ------ | -------- | ---------- | --- | | scalingtoverylargeuseroritemsets. | | | | | Second,the | | | | | | | | | IEEE. | quality | of the generated | | profiles | is | inherently | de- | | | | | | | | | ------- | ----------------- | --- | -------- | --- | ------------- | --- | --------- | ------- | --- | ----------- | ----- | --- | ----- | | pendent | on the underlying | | LLMs | | and prompting | | | | | | | | | | | | | | | | | Yue Chen, | Minghua | | He, Fangkai | Yang, | Pu | Zhao, | strategies. Variationsinmodelcapacityorprompt LuWang,YuKang,YifeiDong,YuefengZhan,Hao Sun,QingweiLin,SaravanRajmohan,andDongmei sensitivitymayleadtodifferencesinprofilestabil- | | | | | | | | Zhang. | 2025. | Warriormath: | | Enhancing | the | mathe- | | --- | --- | --- | --- | --- | --- | --- | ------ | ----- | ------------ | --- | --------- | --- | ------ | ity,whichwedonotexplicitlycontrolinthecurrent maticalabilityoflargelanguagemodelswithadefect- design. Finally,ourevaluationfocusesontext-rich | | | | | | | | awareframework. | | | Preprint,arXiv:2508.01245. | | | | | --- | --- | --- | --- | --- | --- | --- | --------------- | --- | --- | -------------------------- | --- | --- | --- | recommendationscenarioswheresufficienthistor- | | | | | | | | Paul Covington, | | Jay | Adams, and | Emre | Sargin. | 2016. | | ------------ | --- | ---------- | --- | ----------------- | --- | --- | --------------- | --- | --- | ---------- | ---- | ------- | ----- | | ical reviews | are | available. | | The effectiveness | | of | | | | | | | | Deepneuralnetworksforyoutuberecommendations. | the proposed | strategy | | in domains | with | extremely | | | | | | | | | | ------------ | -------- | --- | ---------- | ---- | --------- | --- | --- | --- | --- | --- | --- | --- | --- | InProceedingsofthe10thACMconferenceonrec- sparsetextualsignalsorinnon-textualmodalities ommendersystems,pages191–198. remainstobefurtherexplored. DeepSeek-AI,DayaGuo,DejianYang,HaoweiZhang, JunxiaoSong,RuoyuZhang,RunxinXu,QihaoZhu, ShirongMa,PeiyiWang,XiaoBi,XiaokangZhang, Data,Privacy,andEthicsConsiderations Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhi- | | | | | | | | hong | Shao, | Zhuoshu | Li, Ziyi | Gao, and | 181 | others. | | --- | --- | --- | --- | --- | --- | --- | ----- | ------------ | ------- | ------------- | --------- | --- | ------- | | | | | | | | | 2025. | Deepseek-r1: | | Incentivizing | reasoning | | capa- | Alldatasetsusedinthisworkarepubliclyreleased bilityinllmsviareinforcementlearning. Preprint, arXiv:2501.12948. bytheiroriginalowners,andwefollowtheofficial termsofuseassociatedwitheachdataset,utilizing JiaxinDeng,ShiyaoWang,KuoCai,LejianRen,Qigen thedatasolelyforresearchpurposesaspermitted | | | | | | | | Hu, Weifeng | | Ding, | Qiang Luo, | and | Guorui | Zhou. | | --- | --- | --- | --- | --- | --- | --- | ----------- | --- | ----- | ---------- | --- | ------ | ----- | bytheirrespectivelicenses;forreproducibility,the 2025. Onerec: Unifyingretrieveandrankwithgen- officialdatasetsourcesarecitedinthecorrespond- erativerecommenderanditerativepreferencealign- ment. Preprint,arXiv:2502.18965. ingsectionsofthispaper,andwedonotredistribute anyrawuser-generatedcontentsuchasreviewtext AbhimanyuDubey,AbhinavJauhri,AbhinavPandey, | aspartofthiswork. | | Thisresearchdoesnotattempt | | | | | | | | | | | | | ----------------- | --- | -------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | AbhishekKadian,AhmadAl-Dahle,AieshaLetman, to identify or infer the identity of any individual Akhil Mathur, Alan Schelten, Amy Yang, Angela | | | | | | | | Fan,and1others.2024. | | | Thellama3herdofmodels. | | | | | --- | --- | --- | --- | --- | --- | --- | -------------------- | --- | --- | ---------------------- | --- | --- | --- | user,andpriortotraininganddownstreamprofile arXivpreprintarXiv:2407.21783. | generation, | we | apply text | filtering | | procedures | to | | | | | | | | | ----------- | --- | ---------- | --------- | --- | ---------- | --- | --- | --- | --- | --- | --- | --- | --- | remove direct personal identifiers where applica- YiFang,WenjieWang,YangZhang,FengbinZhu,Qi- ble;themodelsaretrainedsolelyonpubliclyavail- fanWang,FuliFeng,andXiangnanHe.2025. 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Guided profile generation im- Table7: Statisticaldetailsoftheevaluationdatasets. | proves personalization | with llms. | arXiv preprint | | | | | | | | | ---------------------- | ---------- | -------------- | ---------- | ------ | ----------- | --- | ------------ | ------ | ------ | | arXiv:2409.13093. | | | Dataset | #Train | #Valid | | #Test #User | | #Item | | | | | Music | 43,071 | 3,271 | | 1,296 | 4,183 | 2,660 | | | | | Book | 71,972 | 6,144 | | 5,541 13,863 | | 13,515 | | | | | Yelp | 51,497 | 4,757 | | 4,328 | 8,453 | 13,426 | | | | | We conduct | | experiments | | on three | widely | used | real-worlddatasets: | | | | • AmazonMusic(Music): | | | | Thisreferstothe“Dig- | | | | --- | --- | --- | --------------------- | --- | ------ | ------ | -------------------- | --- | ------ | | | | | ital Music” | | subset | of the | well-known | | Amazon | Productdataset3,whichrecordsrichuserreviews, ratings,andtextualinformationaboutitems,such | | | | as titles, | across | a | broad | range | of product | cate- | | --- | --- | --- | ---------- | ------ | --- | ----- | ----- | ---------- | ----- | gories,ontheAmazonplatform.2 | | | | • AmazonBook(Book): | | | Thisreferstothe“Book” | | | | | --- | --- | --- | ------------------- | --- | --- | --------------------- | --- | --- | --- | subsetoftheAmazonProductdataset. dataset4, | | | | • Yelp: | This | refers | to the | Yelp | Open | | | --- | --- | --- | ------- | -------- | -------------- | -------- | ------- | ------------- | --------- | | | | | which | includes | user | reviews, | ratings | | for busi- | | | | | nesses | such | as restaurants | | and | retail shops, | as | wellastextualinformationaboutthebusinesses. Itiswidelyusedinrecommendationtasks(Qiu etal.,2021). WeusetheentireMusicdatasetforexperiments, | | | | while for | the Book | | and Yelp | datasets, | we | utilize | | --- | --- | --- | ------------------------------- | -------- | --- | -------- | --------- | ---------- | ------- | | | | | onlyasubsetduetotheirlargesize. | | | | | FortheBook | | dataset,weusedatafromthelasttwomonths,and fortheYelpdataset,weusedatafromthelastsix months. Foreachdataset,wesplititintotraining, | | | | validation, | and | test | sets based | on | the timestamps | | | --- | --- | --- | ---------------- | --- | -------- | ---------- | ---- | -------------- | --- | | | | | of interactions, | | ensuring | that | test | interactions | oc- | curafteralltrainingandvalidationinteractionsto preventinformationleakage(Jietal.,2023). | | | | Regarding | | data | filtering, | following | | prior | | --- | --- | --- | --------- | --- | ----------- | ---------- | --------- | -------- | ----- | | | | | work (Liu | et | al., 2019), | we | adopt | a 5-core | set- | tingtofilterthedataandexcludecold-startusers | | | | and items—those | | not | appearing | | in the | training | | --- | --- | --- | --------------- | --- | ---------- | --------- | ---- | ------ | -------- | | | | | set—from | the | validation | and | test | sets. | The sta- | tisticaldetailsoftheprocesseddatasetareprovided inTable7. 3https://cseweb.ucsd.edu/~jmcauley/datasets/ amazon/links.html. 4https://business.yelp.com/data/resources/ open-dataset/. | A.1.1 | ImplementationDetails | | | | | | RLMRecPrompt | | | | | ----- | --------------------- | --- | --- | --- | --- | --- | ------------ | --- | --- | --- | Inourexperiments,weprimarilyemployQwen3- Role: Businessrecommendationassistant Task: Determinebusinesstypesauserislikely | 8B(Team,2025)andLLaMA38BInstruct | | | | | | (Dubey | | | | | | ------------------------------------------ | --- | ----- | --- | --- | ------- | ---------- | ------------ | --- | --- | --- | | etal.,2024;Touvronetal.,2023)asboththerec- | | | | | | | toenjoy | | | | | ommendation | | model | and | the | profile | generation | InputFormat: | | | | •Title: Businessname model. Thetrainingprocessisimplementedusing theTRL(vonWerraetal.). Keyhyperparameters, •Categories: Businesscategories suchasbatchsizeandlearningrate,aredetermined •Sentiment: Usersentimenttowardbusiness throughgridsearchtoachieveoptimalperformance. OutputRequirements: Moredetailscanbefoundinourcode. 1. JSONformatonly 2. Structure: A.2 BaselinePrompts { | | | | | | | | "summarization": | "Types | of businesses | user | | --- | --- | --- | --- | --- | --- | --- | ---------------- | ------ | ------------- | ---- | KARPrompt likelyenjoys"(≤100words), Task: Analyzeuserpreferencesbasedonbusi- | | | | | | | | "reasoning": | "Brief explanation | | for summa- | | --- | --- | --- | --- | --- | --- | --- | ------------ | ------------------ | --- | ---------- | nessreviewinghistory rization"(nowordlimit) Input: {user_history}-User’sbusinessreview- } inghistorywithsentimentsovertime 3. NoadditionaltextoutsideJSON Instructions: | 1. | Analyze | the | user’s | preferences | considering | | | | | | | --- | ------- | --- | ------ | ----------- | ----------- | --- | ------ | ----------- | --- | ------ | | | | | | | | | Input: | INTERACTION | | ITEMS: | businessnamesandcategories {user_history} | 2. | Take | into account | | sentiment | patterns | over | | | | | | --- | ---- | ------------ | --- | --------- | -------- | ---- | --- | --- | --- | --- | time | 3. | Provideclearexplanationsbasedonreview- | | | | | | LGPrompt | | | | | --- | -------------------------------------- | --- | --- | --- | --- | --- | -------- | --- | --- | --- | inghistorydetails Youwillserveasanassistanttohelpmegener- | 4. | Consider | other | pertinent | | factors | that may | | | | | | --- | -------- | ----- | --------- | --- | ------- | -------- | --- | --- | --- | --- | ateauserprofilebasedonthisuser’ssentiments influencepreferences historytobetterunderstandthisusers’interest andthuspredicthis/hersentimentaboutatarget | PALRPrompt | | | | | | | item. Iwillprovideyouwithsomebehaviorhis- | | | | | ---------- | --- | --- | --- | --- | --- | --- | ----------------------------------------- | --- | --------------- | --- | | | | | | | | | toryoftheuserinthisformat: | | [itemattributes | | Task: Summarizeuserpreferencesusingkey- | | | | | | | | andsentiment]. | Theuserprofileyougenerate | | | | --- | --- | --- | --- | --- | --- | --- | -------------- | ------------------------- | --- | --- | words. shouldcontainasmuchusefulcontentaspossi- | Input: | | {user_history} | | - historical | businesses | | | | | | | ------ | --- | -------------- | --- | ------------ | ---------- | --- | --- | --- | --- | --- | bletohelppredicttheuser’ssentimenttowards withusersentiments. anewbusiness. | Output | | Format: | An | itemized | list ranked | by | | | | | | ------ | --- | ------- | --- | -------- | ----------- | --- | --- | --- | --- | --- | USERHISTORY:{user_history}. importance. PROFILEYOUGENERATE: Template: | | • KEY_WORD_1: | | | | | "HIS- | | | | | | --- | ------------- | --- | --- | --- | --- | ----- | --- | --- | --- | --- | R4RecPrompt(Reasoner) | | TORY_BUSINESS_1", | | | | | "HIS- | | | | | | --- | ----------------- | --- | --- | --- | --- | ----- | --- | --- | --- | --- | UserReviewHistory TORY_BUSINESS_2" ⟨H u organizedasbelow⟩ | | • KEY_WORD_2: | | | | | "HIS- | 1. TitleofItem1 | | | | | ------------- | ---------------- | --- | --- | --- | --- | ----- | ---------------- | ----------------------- | ----------- | --- | | | TORY_BUSINESS_3" | | | | | | PositiveAspects: | [Aspect1],[Aspect2],... | | | | | | | | | | | NegativeAspects: | [Aspect1],[Aspect2],... | | | | Instructions: | | | | | | | User Preference | Elements: | [Preference | 1], | [Preference2],... | | 1. Extract | key | preference | | indicators | from | | | | | | --- | ---------- | --- | ---------- | --- | ---------- | ---- | --- | --- | --- | --- | 2. TitleofItem2 userinteractionhistory. | | | | | | | | PositiveAspects: | [Aspect1],[Aspect2],... | | | | --- | --- | --- | --- | --- | --- | --- | ---------------- | ----------------------- | --- | --- | 2. Rankkeywordsbyimportance. NegativeAspects: [Aspect1],[Aspect2],... | | | | | | | | User Preference | Elements: | [Preference | 1], | | --- | --- | --- | --- | --- | --- | --- | --------------- | --------- | ----------- | --- | | [Preference2],... | | | | | | | cantlyworsethanDUETacrossalldatasets. | | | | | This | | ----------------- | --- | --- | --- | --- | --- | --- | ------------------------------------- | --- | --- | --- | --- | ---- | indicatesthatcoherentsummariesaloneareinsuf- ... | | | | | | | | ficient, | and that | the gains | of DUET | | arise from | | --- | --- | --- | --- | --- | --- | --- | -------- | -------- | --------- | ------- | --- | ---------- | ItemReviewHistorybyOtherUsers ⟨H organizedinthesameformatasabove⟩ learnedsemanticabstractionratherthanextractive i compression. | Task: | Analyze | whether | | the user | will | like the | | | | | | | | --------- | ------- | ------- | --- | ---------- | ----------- | -------- | --- | --- | --- | --- | --- | --- | | new Music | | i based | on | the user’s | preferences | | | | | | | | B.2 RankingunderHardNegativeSampling | andtheitem’sfeatures. | | | | Provideyourrationale | | | | | | | | | | --------------------- | --- | --- | --- | -------------------- | --- | --- | --- | --- | --- | --- | --- | --- | Toconstructamorechallengingrankingscenario, inoneconciseparagraph. wereplacerandomnegativeswithhardnegatives | | | | | | | | generatedbyacollaborativefilteringmodel. | | | | | Specif- | | --- | --- | --- | --- | --- | --- | --- | ---------------------------------------- | --- | --- | --- | --- | ------- | A.3 ExampleofCue ically,foreachuser,weretrievehigh-scoringitems Thefollowingexamplesillustratehowrawsignals | | | | | | | | from an | EASE(Steck, | 2019) | model | that | the user | | --------------------- | --- | --- | ---------------- | --- | --- | ---- | ------- | ----------- | ----- | ----- | ---- | -------- | | aredistilledintocues. | | | Asshownonthetop, | | | user | | | | | | | hasnotinteractedwith,andcombinethemwiththe cuesemphasizehistoricalpreferences,whileitem ground-truthitemtoformthecandidateset. cues highlight metadata and user-group patterns. Table 9 reports the results. Compared to ran- | Together, | they | provide | minimal | | but informative | | | | | | | | | --------- | ---- | ------- | ------- | --- | --------------- | --- | --- | --- | --- | --- | --- | --- | domsampling,performancedecreasesforallmeth- hypothesesforprofileexploration. | | | | | | | | ods due | to increased | difficulty, | while | DUET | con- | | --- | --- | --- | --- | --- | --- | --- | --------- | ------------ | ----------- | ---------------- | ---- | ------ | | | | | | | | | sistently | maintains | the | best performance | | across | ExamplesofUserCues | | | | | | | | datasets. | Thisindicatesthattheimprovementsare | | | | | | ------- | ----- | ------ | ------ | --- | ------- | -------- | --------- | ----------------------------------- | --- | --- | --- | --- | | “enjoys | retro | puzzle | games” | — | derived | from re- | | | | | | | robustandnotlimitedtotrivialrankingscenarios. peatedengagementwithclassictitles. “prefersconciseproductreviews”—inferredfrom B.3 RobustnessunderPreferenceDiversity apatternofshort,directcomments. WefurtheranalyzetherobustnessofDUETunder “tendstogivehighratingsbutrarelycomments”— | | | | | | | | varyinglevelsofuserpreferencediversity. | | | | | Weuse | | --- | --- | --- | --- | --- | --- | --- | --------------------------------------- | --- | --- | --- | --- | ----- | highlightingconsistencybutlimitedfeedback. thevarianceofhistoricalratingsasaproxyforpref- | | | | | | | | erencestability: | | lowvarianceindicatesconsistent | | | | | --- | --- | --- | --- | --- | --- | --- | ---------------- | --- | ------------------------------ | --- | --- | --- | ExamplesofItemCues | | | | | | | | preferences, | while | high | variance | corresponds | to | | --- | --- | --- | --- | --- | --- | --- | ------------ | ----- | ---- | -------- | ----------- | --- | “lightweighttrail-runningshoes”—derivedfrom diverseorpotentiallyconflictingsignals. productmetadata. Wepartitionusersintothreegroupsbasedonper- | “popular | among | budget-conscious | | | users” | — in- | | | | | | | | -------- | ----- | ---------------- | --- | --- | ------ | ----- | --- | --- | --- | --- | --- | --- | centilethresholds(bottom33%,middle33%,top ferredfrompurchasepatterns. 33%ofratingvariance)andevaluateperformance | “stylized | with | retro | aesthetics” | | — extracted | from | | | | | | | | --------- | ---- | ----- | ----------- | --- | ----------- | ---- | --- | --- | --- | --- | --- | --- | withineachgroup. itemdescriptions. | | | | | | | | As shown | in | Table 10, | performance | | degrades | | --- | --- | --- | --- | --- | --- | --- | -------- | --- | --------- | ----------- | --- | -------- | smoothlyaspreferencediversityincreasesacross | | | | | | | | all datasets. | Importantly, | | the degradation | | is grad- | | --- | --- | --- | --- | --- | --- | --- | ------------- | ------------ | --- | --------------- | --- | -------- | B AdditionalExperiments ualratherthancatastrophic,indicatingthatDUET B.1 Non-LLMBaselineviaExtractive remainsstableunderheterogeneousornoisyinter- Summarization actionhistories. | To examine | | whether | the | gains | of DUET | stem | | | | | | | | ---------- | --- | ------- | --- | ----- | ------- | ---- | --- | --- | --- | --- | --- | --- | C TheUseofLargeLanguageModels | from improved | | semantic | | representations | | rather | | | | | | | | ------------- | --- | ---------------- | --- | --------------- | --------- | ------ | --- | --- | --- | --- | --- | --- | | than generic | | text generation, | | we | introduce | a non- | | | | | | | WeusedaLargeLanguageModel(LLM)onlyas LLMbaselinebasedonextractivesummarization. a writing assistant to polish the language of the | Specifically, | | we apply | TextRank | | (Mihalcea | and | | | | | | | | ------------- | --- | -------- | -------- | --- | --------- | --- | ---------- | ------ | ------- | ----------- | --- | --------- | | | | | | | | | manuscript | (e.g., | grammar | refinement, | | style ad- | Tarau,2004)toselectsalientsentencesfromuser | | | | | | | | justment,andclarityimprovement). | | | | Theresearch | | | --- | --- | --- | --- | --- | --- | --- | -------------------------------- | --- | --- | --- | ----------- | --- | historiesandconstructuserprofileswithoutusing ideas,methodologydesign,experiments,andanal- anygenerativemodel. ysiswereentirelyconceived,implemented,andval- | The | extracted | summaries | | are | then fed | into the | | | | | | | | --- | --------- | --------- | --- | --- | -------- | -------- | --- | --- | --- | --- | --- | --- | idatedbytheauthorswithoutrelianceontheLLM. samedownstreampredictorforratingestimation. | | | | | | | | The LLM | did not | contribute | to research | | ideation, | | --- | --- | --- | --- | --- | --- | --- | ------- | ------- | ---------- | ----------- | --- | --------- | Thisbaselineisolatestheeffectofreadablesumma- experimentaldesign,orresultinterpretation. rizationfromrepresentationlearning. Table8showsthatTextRankimprovesoversim- | ple history | truncation | | (10H), | but | remains | signifi- | | | | | | | | ----------- | ---------- | --- | ------ | --- | ------- | -------- | --- | --- | --- | --- | --- | --- | | Method | | Yelp | | | AmazonMusic | | | AmazonBooks | | | ------ | -------- | ---- | ----- | --- | ----------- | --------- | --- | ----------- | --------- | | | MAE RMSE | Acc | F1(%) | MAE | RMSE | Acc F1(%) | MAE | RMSE | Acc F1(%) | | | | (%) | | | | (%) | | | (%) | 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 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 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 Table8: Comparisonwithanon-LLMextractivesummarizationbaseline(TextRank). | 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 | | Table9: | RankingperformanceunderEASE-based(Steck,2019)hardnegatives. | | | | | | | | | ------------- | ------- | ----------------------------------------------------------- | --- | --- | ----------- | --- | --- | ----------- | --- | | VarianceGroup | | Yelp | | | AmazonMusic | | | AmazonBooks | | #Samp. MAE RMSE Acc F1 #Samp. MAE RMSE Acc F1 #Samp. MAE RMSE Acc F1 | | | | (%) | (%) | | (%) (%) | | | (%) (%) | | --- | --- | --- | --- | --- | --- | ------- | --- | --- | ------- | 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 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 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 Table10: PerformanceofDUETunderdifferentlevelsofuserpreferencediversity(measuredbyratingvariance). #Samp. denotesthenumberofsamplesineachgroup.