--- license: cc-by-nc-4.0 language: - en pretty_name: E-commerce User Simulation — corpus and evaluation data size_categories: - 10K/`** — 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 ```python 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: ``` user You are an advanced user simulator. [...] # Category # User Persona # 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. 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`; `rating_number` to `int64` (lossless cast: zero values had a fractional part); `categories` and `bought_together` to `list`. - `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 ```bibtex @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