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home / papers / 2026 / OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation

OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation

Paper · 2026 · Ziyi Wang, Yuxuan Lu, Wenbo Li et al.

Summary

LLM agents can produce plausible web behavior, but it is hard to check whether they reproduce what a specific person would actually do. Public data that pairs real users' step-by-step actions with their reasons and profiles has been missing. The authors address this gap with OPeRA, an online shopping dataset built from real participants. A custom Chrome extension, ShoppingFlow, logged clicks, inputs, scrolls and page changes on Amazon together with HTML and screenshots. At decision points it occasionally asked users to explain their choice. A questionnaire and optional interviews supplied persona profiles covering demographics, shopping style and personality. Using this data, the authors define a benchmark: given a persona and the history of observations, actions and rationales, predict a user's next action and its rationale. They tested four LLMs, and all scored low on exact next-action accuracy. Including rationales helped most models, while personas mainly improved action-type classification. The models also tended to keep pushing toward a purchase instead of quitting a session early, as real users often do. OPeRA offers a grounded testbed for building personalized "digital twin" agents.

Key points

  • Dataset sizes:
    • OPeRA-full: 692 sessions from 51 users collected over four weeks, with 28,904 <action, observation> pairs and 604 user-provided rationales.
    • OPeRA-filtered: 527 sessions, 5,856 pairs and 207 rationales.
    • The dataset is released on Hugging Face as NEU-HAI/OPeRA.
  • Models and accuracy:
    • GPT-4.1, DeepSeek-R1, Claude-3.7 and Llama-3.3 were evaluated on OPeRA-test.
    • Exact next-action accuracy stayed low, for example about 10.75% for Claude-3.7 and 8.76% for Llama-3.3.
  • Input ablations:
    • Dropping historical rationales usually hurt performance, especially outcome prediction. Llama-3.3 was an exception.
    • Adding personas improved action-type and click-type prediction but not exact action accuracy.
  • Error analysis:
    • Clicking the wrong button accounted for roughly 61–76% of errors across models.
    • The terminate action was seldom predicted, with Claude and Llama never producing it.
  • Limitations:
    • The benchmark uses a reduced action space without scrolling or navigation.
    • It relies only on text and ignores the collected screenshots.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Columbia UniversityNew York · United States241 works · 54,053 citations🎓University of Southern CaliforniaLos Angeles · United States162 works · 35,836 citations🎓The Ohio State UniversityColumbus · United States82 works · 18,502 citations🎓Northeastern UniversityBoston · United States57 works · 6,533 citations🎓Stony Brook UniversityStony Brook · United States43 works · 2,571 citations

 Fields, topics and concepts

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton and Dakuo Wang. 2026. OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.2033

@inproceedings{wang2026opera,
  title = {{OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation}},
  author = {Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton and Dakuo Wang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.2033},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2033}
}

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