| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| - text-classification |
| language: |
| - en |
| tags: |
| - personalization |
| - user-profiles |
| - persona |
| - recommendation |
| pretty_name: Behaviorally Grounded User Profiles |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: open_ended |
| data_files: open_ended_profiles.csv |
| default: true |
| - config_name: synthetic_baseline |
| data_files: synthetic_baseline_profiles.csv |
| --- |
| |
| # Behaviorally Grounded User Profiles from the Wild |
|
|
| Open-ended, anonymized user profiles distilled from authentic social-media behavior, released with the paper |
| **"Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning."** |
|
|
| Persona-driven methods for personalizing LLMs typically rely on rigid synthetic personas built from a small set of |
| categorical attributes (age, gender, nationality). These flatten individual variation and lean on stereotypes. This |
| dataset instead provides **open-ended profiles extracted from real behavioral traces**: short, coherent textual bios |
| synthesized from users' historical social-media posts. Alongside them we release a **synthetic baseline** generated by |
| prompting an LLM, so the two can be compared directly. |
|
|
| ## How the data was created |
|
|
|  |
|
|
| ## Dataset structure |
|
|
| Two configurations, each a CSV with the same schema: |
|
|
| | Column | Type | Description | |
| | -------------- | ------ | ------------------------------------------------------------------ | |
| | `user_id` | string | Pseudonymous random UUID; no real handle | |
| | `user_profile` | string | A short free-text bio describing the user's interests and traits | |
|
|
| | Config | File | Rows | Description | |
| | -------------------- | -------------------------------- | ---- | ----------------------------------------------------------------------- | |
| | `open_ended` | `open_ended_profiles.csv` | 824 | Behaviorally grounded profiles extracted from real Bluesky post histories | |
| | `synthetic_baseline` | `synthetic_baseline_profiles.csv`| 842 | Purely synthetic profiles from LLM prompting | |
|
|
| Open-ended profiles average ~116 words; synthetic baseline profiles ~210 words. |
|
|
| ### Example (open-ended) |
|
|
| ```json |
| { |
| "user_id": "5078790f-63fe-4d2a-a116-60ec37c17263", |
| "user_profile": "The person is a nature-loving individual who enjoys flowers, music, and a wide variety of foods, with a particular fondness for chicken." |
| } |
| ``` |
|
|
| ## Results |
|
|
| Downstream results for the Qwen3 models, comparing **No Profile** (base model), the **Synthetic** baseline, and our |
| **Open-Ended** behaviorally grounded profiles. RecBench columns (Netflix, Books, News) report F1; URS columns |
| (Leisure, Creativity, Advice, Avg.) report the 1–10 LLM-judge score. Higher is better; **bold** = best per column |
| within each model. (Full results with additional models are in the paper and the accompanying code repository.) |
|
|
| | Model | Variant | Netflix (F1) | Books (F1) | News (F1) | Leisure | Creativity | Advice | Avg. | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| | Qwen3-8B | No Profile | 0.421 | 0.515 | 0.318 | 5.48 | 5.31 | 5.99 | 5.59 | |
| | | Synthetic | 0.420 | 0.625 | 0.319 | 6.34 | 6.72 | 7.08 | 6.72 | |
| | | Open-Ended | **0.450** | **0.649** | **0.322** | **6.76** | **7.40** | **7.65** | **7.27** | |
| | Qwen3-14B | No Profile | 0.419 | 0.308 | 0.303 | **7.49** | 7.90 | 8.06 | 7.82 | |
| | | Synthetic | 0.416 | 0.538 | **0.327** | 7.06 | 7.54 | 7.91 | 7.50 | |
| | | Open-Ended | **0.459** | **0.632** | 0.321 | 7.29 | **8.10** | **8.27** | **7.88** | |
| | Qwen3-32B | No Profile | 0.403 | 0.569 | 0.308 | 6.79 | 7.09 | 6.90 | 6.93 | |
| | | Synthetic | 0.427 | 0.580 | **0.317** | 7.20 | 7.87 | 8.06 | 7.71 | |
| | | Open-Ended | **0.455** | **0.658** | 0.315 | **7.35** | **8.06** | **8.23** | **7.88** | |
|
|
| ## Profile diversity |
|
|
|  |
|
|
| *Birth-location distribution of the **baseline synthetic** profiles (left) and our **open-ended** behaviorally |
| grounded profiles (right). Synthetic personas collapse toward a narrow set of nationalities, while the open-ended |
| profiles maintain a long-tailed, representative distribution (top-12 countries shown; see the paper for the full |
| comparison and categorical entropy analysis).* |
|
|
| ## Source data & licensing |
|
|
| Profiles are derived from the public [**"2 Million Bluesky Posts"**](https://huggingface.co/datasets/alpindale/two-million-bluesky-posts) corpus, released under **Apache 2.0**. |
| Collection followed the platform's Terms of Service and API guidelines. This derived dataset is released under |
| **Apache 2.0**. |
|
|
|
|
| ## Citation |
| This paper has been accepted to AACL-IJCNLP 2026. |
| The camera-ready version will be released later. |
| If you find this work useful, please cite the current arXiv version: |
| ```bibtex |
| @article{li2026behaviorally, |
| title={Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning}, |
| author={Li, Yuxuan and Zhong, Victor and Kamalloo, Ehsan}, |
| journal={arXiv preprint arXiv:2609.00014}, |
| year={2026} |
| } |
| ``` |
|
|
| Please also cite the source corpus (Alpin Dale, "2 Million Bluesky Posts", 2024). |
|
|
