submission_id stringlengths 36 36 | user_id stringlengths 36 36 | prolific_pid stringlengths 24 24 | study_id stringclasses 1
value | session_id stringlengths 24 24 | start_time float64 1.78B 1.78B | study_type stringclasses 1
value | demographics dict | background dict | _pool_index int64 0 49 | _pool_category stringclasses 1
value | pair_id stringlengths 36 36 | category stringclasses 1
value | product_a dict | product_b dict | familiarity_a stringclasses 3
values | familiarity_b stringclasses 3
values | pre_rating int64 1 7 | post_rating int64 1 7 | rating_delta int64 -6 2 | conversation dict | reflection dict | model_name stringclasses 1
value | sampler_path stringclasses 1
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"description": [
"It's time for a decade's worth of Real World casts to come clean. MTV presents a de cade of crazy bloopers certain to make you laugh, cry, or squirm. These embarrassing, ... | Never heard of it | Heard of it, but not used/purchased | 5 | 5 | 0 | {
"system_prompt": "You are a warm, knowledgeable sales agent for an Amazon product. Your goal is to persuade the user to prefer your product over a competitor's product — but ONLY using what is in the two product listings below. Anything not listed does not exist and cannot be offered, mentioned, or implied for eith... | {
"standout_moment": "It implied that my assumptions about its preferred product were wrong but said it could not correct them. This just left me confused. It's opening comment about following someone on the job vs. watching meaningless bloopers was good, but it could not seem to continue on from my counter to it.... | meta-llama/Llama-3.1-8B-Instruct | tinker://6b84838d-ec56-5d1f-b964-75588d306470:train:0/sampler_weights/000150 | {
"personalization": false
} |
3e6426d0-b539-4994-9ca4-e7872a5e0b08 | 42a61e58-6c9a-4f95-9363-ec56e3fd72d9 | 62c026818910950fc380e599 | 69ea8178171d05dfa669c116 | 69ebaee2f437d6609ee5aa9b | 1,777,053,427.670768 | preference | {
"age": "49",
"gender": "Male",
"geographic_region": "Midwest",
"education_level": "College graduate/some postgrad",
"race": "White",
"us_citizen": "Yes",
"marital_status": "Never been married",
"religion": "Nothing in particular",
"religious_attendance": "Never",
"political_affiliation": "Democrat... | {
"movies_criteria": "The genre mostly. I like quiet indie films. There are a few specific film makers I like and I'll watch anything by them, but mostly it's a \"tone\" I'm after.",
"movies_enjoy": "I like movies that are either funny dramas or dramatic comedies. I like stories about real people, who are recogniab... | 10 | movies | 262ad047-8e5f-4e68-baec-d115bf1ec3d4 | movies | {
"id": "948a3578-93b0-4754-a794-740be3033a69",
"asin": "B0001US5RO",
"title": "Kaleido Star - Welcome to the Kaleido Star (Vol. 1)",
"description": [
"Although she's something of a klutz and has never studied ballet or trapeze, 16-year-old Sora Naegino comes to America from Japan to try out for the Cirque ... | {
"id": "7bbfcdbc-5bda-48e1-952d-6ff9df061f67",
"asin": "B00009B8DG",
"title": "El Hazard: The Wanderers - The DVD Collection",
"description": [
"The broadcast version ofEl Hazard(1996) repeats most of the characters and situations from the popular OVA series (1995). When Jinnai sabotages Makoto's science... | Never heard of it | Never heard of it | 1 | 1 | 0 | {
"system_prompt": "You are a warm, knowledgeable sales agent for an Amazon product. Your goal is to persuade the user to prefer your product over a competitor's product — but ONLY using what is in the two product listings below. Anything not listed does not exist and cannot be offered, mentioned, or implied for eith... | {
"standout_moment": "Nothing really. It's text wasn't incoherent, but it didn't make a lot of sense either. I wasn't sure how to respond.",
"thinking_change": "I couldn't come up with any reason to see the second film, so I increased my interest in my initial choice."
} | meta-llama/Llama-3.1-8B-Instruct | tinker://6b84838d-ec56-5d1f-b964-75588d306470:train:0/sampler_weights/000150 | {
"personalization": false
} |
End of preview. Expand in Data Studio
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0423_4_2
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level fields (prolific_pid, demographics, background) are duplicated across rows that share a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
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