Datasets:
Tasks:
Text Generation
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
conversational-recommendation
user-simulation
recommender-systems
amazon-reviews-2023
recsys2026
License:
| 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 | |
| ```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: | |
| ``` | |
| <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 | |
| ```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 | |