Hugging Face's logo

Datasets:
planeB
/
ecommerce-user-sim

Tasks:
Text Generation
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
conversational-recommendation
user-simulation
recommender-systems
amazon-reviews-2023
recsys2026
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
ecommerce-user-sim / README.md
planeB's picture
planeB
Initial release: teacher-student corpus (10 domains) + evaluation data (4 domains)
3c0d381 verified
preview code
|
Raw History Blame Contribute Delete
14.9 kB
metadata 
license: cc-by-nc-4.0
language:
  - en
pretty_name: E-commerce User Simulation — corpus and evaluation data
size_categories:
  - 10K<n<100K
task_categories:
  - text-generation
tags:
  - conversational-recommendation
  - user-simulation
  - recommender-systems
  - amazon-reviews-2023
  - recsys2026
configs:
  - config_name: corpus_items
    data_files:
      - split: All_Beauty
        path: corpus/items/All_Beauty.parquet
      - split: Baby_Products
        path: corpus/items/Baby_Products.parquet
      - split: Beauty_and_Personal_Care
        path: corpus/items/Beauty_and_Personal_Care.parquet
      - split: CDs_and_Vinyl
        path: corpus/items/CDs_and_Vinyl.parquet
      - split: Cell_Phones_and_Accessories
        path: corpus/items/Cell_Phones_and_Accessories.parquet
      - split: Electronics
        path: corpus/items/Electronics.parquet
      - split: Movies_and_TV
        path: corpus/items/Movies_and_TV.parquet
      - split: Office_Products
        path: corpus/items/Office_Products.parquet
      - split: Sports_and_Outdoors
        path: corpus/items/Sports_and_Outdoors.parquet
      - split: Video_Games
        path: corpus/items/Video_Games.parquet
  - config_name: corpus_users
    data_files:
      - split: All_Beauty
        path: corpus/users/All_Beauty.parquet
      - split: Baby_Products
        path: corpus/users/Baby_Products.parquet
      - split: Beauty_and_Personal_Care
        path: corpus/users/Beauty_and_Personal_Care.parquet
      - split: CDs_and_Vinyl
        path: corpus/users/CDs_and_Vinyl.parquet
      - split: Cell_Phones_and_Accessories
        path: corpus/users/Cell_Phones_and_Accessories.parquet
      - split: Electronics
        path: corpus/users/Electronics.parquet
      - split: Movies_and_TV
        path: corpus/users/Movies_and_TV.parquet
      - split: Office_Products
        path: corpus/users/Office_Products.parquet
      - split: Sports_and_Outdoors
        path: corpus/users/Sports_and_Outdoors.parquet
      - split: Video_Games
        path: corpus/users/Video_Games.parquet
  - config_name: corpus_sft_simulator
    data_files:
      - split: All_Beauty
        path: corpus/sft_simulator/All_Beauty.parquet
      - split: Baby_Products
        path: corpus/sft_simulator/Baby_Products.parquet
      - split: Beauty_and_Personal_Care
        path: corpus/sft_simulator/Beauty_and_Personal_Care.parquet
      - split: CDs_and_Vinyl
        path: corpus/sft_simulator/CDs_and_Vinyl.parquet
      - split: Cell_Phones_and_Accessories
        path: corpus/sft_simulator/Cell_Phones_and_Accessories.parquet
      - split: Electronics
        path: corpus/sft_simulator/Electronics.parquet
      - split: Movies_and_TV
        path: corpus/sft_simulator/Movies_and_TV.parquet
      - split: Office_Products
        path: corpus/sft_simulator/Office_Products.parquet
      - split: Sports_and_Outdoors
        path: corpus/sft_simulator/Sports_and_Outdoors.parquet
      - split: Video_Games
        path: corpus/sft_simulator/Video_Games.parquet
  - config_name: corpus_dialogues
    data_files:
      - split: All_Beauty
        path: corpus/dialogues/All_Beauty.parquet
      - split: Baby_Products
        path: corpus/dialogues/Baby_Products.parquet
      - split: Beauty_and_Personal_Care
        path: corpus/dialogues/Beauty_and_Personal_Care.parquet
      - split: CDs_and_Vinyl
        path: corpus/dialogues/CDs_and_Vinyl.parquet
      - split: Cell_Phones_and_Accessories
        path: corpus/dialogues/Cell_Phones_and_Accessories.parquet
      - split: Electronics
        path: corpus/dialogues/Electronics.parquet
      - split: Movies_and_TV
        path: corpus/dialogues/Movies_and_TV.parquet
      - split: Office_Products
        path: corpus/dialogues/Office_Products.parquet
      - split: Sports_and_Outdoors
        path: corpus/dialogues/Sports_and_Outdoors.parquet
      - split: Video_Games
        path: corpus/dialogues/Video_Games.parquet
  - config_name: eval_items
    data_files:
      - split: All_Beauty
        path: eval/items/All_Beauty.parquet
      - split: Clothing_Shoes_and_Jewelry
        path: eval/items/Clothing_Shoes_and_Jewelry.parquet
      - split: Electronics
        path: eval/items/Electronics.parquet
      - split: Movies_and_TV
        path: eval/items/Movies_and_TV.parquet
  - config_name: eval_users
    data_files:
      - split: All_Beauty
        path: eval/users/All_Beauty.parquet
      - split: Clothing_Shoes_and_Jewelry
        path: eval/users/Clothing_Shoes_and_Jewelry.parquet
      - split: Electronics
        path: eval/users/Electronics.parquet
      - split: Movies_and_TV
        path: eval/users/Movies_and_TV.parquet
  - config_name: eval_catalog
    data_files:
      - split: All_Beauty
        path: eval/catalog/All_Beauty.parquet
      - split: Clothing_Shoes_and_Jewelry
        path: eval/catalog/Clothing_Shoes_and_Jewelry.parquet
      - split: Electronics
        path: eval/catalog/Electronics.parquet
      - split: Movies_and_TV
        path: eval/catalog/Movies_and_TV.parquet
  - config_name: eval_prompt_components
    data_files:
      - split: All_Beauty
        path: eval/prompt_components/All_Beauty/*.parquet
      - split: Clothing_Shoes_and_Jewelry
        path: eval/prompt_components/Clothing_Shoes_and_Jewelry/*.parquet
      - split: Electronics
        path: eval/prompt_components/Electronics/*.parquet
      - split: Movies_and_TV
        path: eval/prompt_components/Movies_and_TV/*.parquet

E-commerce User Simulation — corpus and evaluation data

Supporting data for two papers on user simulators for conversational recommender systems:

  • Simulating Diverse User Behavioral Stereotypes for Evaluating Agentic Conversational Recommenders (RecSys '26) — teacher–student corpus and evaluation data
  • Beyond the Lookup: Simulating Realistic User Uncertainty for the Evaluation of Conversational Agentic Recommenders (Information Systems Frontiers, 2026)

Code: https://github.com/swapUniba/Simulating-Diverse-User-Behavioral-Stereotypes

The repository is split into two families, mirroring the two folders of the code repository.

Family Mirrors Contents
corpus/ SyntenticData/ teacher–student training corpus, 10 domains
eval/ PreferenceCoverage/ evaluation protocol data, 4 domains

corpus/ — teacher–student corpus (10 domains)

Domain items users SFT examples dialogues
All_Beauty 4,731 913 8,708 1,826
Baby_Products 3,247 387 3,662 774
Beauty_and_Personal_Care 4,052 396 3,766 792
CDs_and_Vinyl 10,259 999 9,144 1,998
Cell_Phones_and_Accessories 4,435 440 4,320 880
Electronics 4,781 456 4,469 912
Movies_and_TV 4,686 378 3,546 756
Office_Products 3,663 438 4,166 876
Sports_and_Outdoors 4,320 413 3,945 826
Video_Games 3,497 502 4,769 1,004
Total 47,671 5,322 50,495 10,644
  • corpus_sft_simulator — the 50,495 examples the student simulator was distilled on. prompt is the complete Gemma-formatted string, including the model turn: it is exactly the input the released checkpoints saw during training.
  • corpus_dialogues — the 10,644 teacher-generated dialogues, with ending_condition (accept / reject), recommend_target_in_list, unnatural_assistant, and the inlined target_item.
  • corpus_items — 47,671 items with visual_description (a textual rendering of the product image) and generated_description.
  • corpus_users — the 5,322 users the corpus draws on, with their target and interaction_history.

eval/ — evaluation protocol (4 domains)

100 users x 4 domains x 3 stereotypes = 1,200 evaluation episodes.

Domain items users prompt components catalog
All_Beauty 335 100 300 356
Clothing_Shoes_and_Jewelry 260 100 300 132,043
Electronics 492 100 300 4,117
Movies_and_TV 1,457 100 300 4,283
Total 2,544 400 1,200 140,799
  • eval_users — the 100 users per domain: inferred profile (profile_*), opening instruction, target item, and history.
  • eval_items — items with visual_description + generated_description, filtered to the ASINs the episodes reference (targets and history). This is the artifact required by PreferenceCoverage/phase1_extract_aspects.py.
  • eval_catalog — the full all_products.json: the retrieval pool the evaluated recommenders search over. Plain Amazon metadata, without the generated descriptions.
  • eval_prompt_components — prompt components per stereotype, 100 per cell; user_persona distinguishes the stereotype within each split.
  • eval/personalwab/<Domain>/ — the four original JSON files (users_ids, user_profiles, user_history, user_instructions) verbatim, for PersonalWAB-style loaders. user_instructions has test (100) and train (0).

Quickstart 

from datasets import load_dataset

# the 100 evaluation users of one domain
users = load_dataset("swap-uniba/ecommerce-user-sim", "eval_users", split="Electronics")

# items with the descriptions Preference Coverage needs
items = load_dataset("swap-uniba/ecommerce-user-sim", "eval_items", split="Electronics")

# the exact string the simulator was trained on
sft = load_dataset("swap-uniba/ecommerce-user-sim", "corpus_sft_simulator", split="Electronics")
print(sft[0]["prompt"])

The simulator prompt has the following structure, and it is the only format the released checkpoints expect:

<bos><start_of_turn>user
You are an advanced user simulator. [...]
# Category
<domain>
# User Persona
<SPECIFIC | VAGUE | VAGUE_TO_SPECIFIC>
# Interaction history
## Item Name: ...
## Description: ...
## Visual Description: ...
## Review: ...
  (repeated for every item in the history)
# Target item
## Item Name: ...
## Description: ...
## Visual Description: ...
## Review: ...

Generate the first user message based on the above information.<end_of_turn>
<start_of_turn>model

The simulator consumes text only: product images enter exclusively as visual_description.

Stereotypes: code labels vs. paper labels

In the code and in this data In the paper
SPECIFIC Direct
VAGUE_TO_SPECIFIC Vague-Proactive
VAGUE Vague-Reactive

Mapping to the names used by the scripts

The scripts in the code repository read directories named historically. This is the map:

Name in the scripts / on disk Config here
items_used_with_descriptions_with_descriptions eval_items
items_used_with_images_with_descriptions_with_descriptions corpus_items
items_used_with_descriptions (in the PreferenceCoverage README) eval_items
user_profiles_cleaned eval_users
user_profiles_filtered_cleaned corpus_users
processed_prompts_new.jsonl corpus_sft_simulator
generated_responses_new.jsonl corpus_dialogues
all_products.json eval_catalog

Product images

Product images are not redistributed. Their textual rendering (visual_description) is in the *_items configs and is all the pipeline ever consumes. The images column preserves the original Amazon URLs (hi_res, large, thumb, variant); to regenerate the JPEGs locally, use download_images.py in the code repository. The local_image_path column present in the sources has been dropped: it held absolute paths from the development machine.

Normalizations applied

The sources diverged across domains; here every config has an identical schema on all splits.

  • details converted to a JSON string (in the sources it was a struct with hundreds of fields in 2 of 10 domains, and a Python repr in the remaining 8).
  • description converted to list<string>; rating_number to int64 (lossless cast: zero values had a fractional part); categories and bought_together to list<string>.
  • images reduced to the four canonical keys. The resolution variants (360w...1920w), present only in Movies_and_TV, were all null.

Known limitations

  • corpus_users contains only the users the corpus actually draws on. The sources held 5,949, of which 627 are leftovers that no SFT example or dialogue references (621 of them in Movies_and_TV) and whose items were never enriched. Filtering them yields the 5,322 users reported in the paper.
  • Every target resolves in *_items, in every domain. A few interaction_history ASINs, however, have no corresponding item row; the history_unresolved column counts them per user. Affected users: Movies_and_TV 134, CDs_and_Vinyl 11, Office_Products 6, Cell_Phones_and_Accessories 2, Sports_and_Outdoors 2.
  • eval_items is filtered to the ASINs the episodes reference. The full pool can be regenerated with process_personalwab/main.py.
  • eval_catalog has no visual_description / generated_description: it is the retrieval pool, not the item dataset the metric needs.
  • Multi-turn simulation logs are not included; they are reproducible with the code.

Simulator checkpoints

Model Hugging Face
Gemma-3 270M https://huggingface.co/planeB/user_gemma_3_270m_it
Gemma-3 1B https://huggingface.co/planeB/user_gemma_3_1b_it
Gemma-3 4B https://huggingface.co/planeB/user_gemma_3_4b_it
Gemma-3 12B https://huggingface.co/planeB/user_gemma_3_12b_it
Gemma-3 27B https://huggingface.co/swap-uniba/user_gemma_3_27b_it

The weights are subject to the Gemma license terms, independent of the license of this data.

Provenance and license

Derived from Amazon Reviews 2023 (McAuley Lab, UCSD). Reviews and product metadata remain subject to the terms of the original source; the textual descriptions (generated_description, visual_description) and the dialogues are LLM-generated. Released under CC BY-NC 4.0 for research use.

Citation 

@inproceedings{PetruzzelliMartinaSimulating,
  author    = {Petruzzelli, Alessandro and Martina, Alessandro Francesco Maria and Musto, Cataldo and de Gemmis, Marco and Lops, Pasquale and Semeraro, Giovanni},
  editor    = {Konstan, Joseph A. and Karypis, George and Adomavicius, Gediminas and Chen, Minmin and Goethals, Bart and Willemsen, Martijn C.},
  title     = {{Simulating Diverse User Behavioral Stereotypes for Evaluating Agentic Conversational Recommenders}},
  booktitle = {Proceedings of the 20th {ACM} Conference on Recommender Systems, RecSys 2026, Minneapolis, Minnesota, USA, September 28-October 2, 2026},
  publisher = {{ACM}},
  year      = {2026},
  doi       = {10.1145/3773078.3831840},
  url       = {https://doi.org/10.1145/3773078.3831840}
}

@article{BeyondLookup,
  author     = {Petruzzelli, Alessandro and Martina, Alessandro Francesco Maria and Musto, Cataldo and de Gemmis, Marco and Lops, Pasquale and Semeraro, Giovanni},
  journal    = {Information Systems Frontiers},
  title      = {Beyond the Lookup: Simulating Realistic User Uncertainty for the Evaluation of Conversational Agentic Recommenders},
  year       = {2026},
  url        = {https://doi.org/10.1007/s10796-026-10787-3}
}

Mirror: planeB/ecommerce-user-sim