Datasetpublic

UWaterloo/behavior_grounding

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… See the full description on the dataset page: https://huggingface.co/datasets/UWaterloo/behavior_grounding.

sourceHugging Faceapache-2.0updated 4d agoView on Hugging Face

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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

[image]

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

[image]

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"** 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).