README.md

---
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