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Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction

Paper · 2026 · Sejun Park, Yoonah Park, Jongwon Lim et al.

Summary

Whether a message sways someone depends heavily on who that person is. Yet systems that judge persuasiveness have no principled way to use a person's earlier online activity. Park et al. study this on the ChangeMyView (CMV) subreddit. The task is to decide whether a reply earned a delta from the post's author, given that author's prior Reddit posts and comments.

Their framework, ReCAP, has two trainable parts:

  • a query generator that reads the original post and writes search queries to pull relevant records from the user's history;
  • an LLM profiler that condenses those records into a post-conditioned textual profile for a frozen predictor LLM.

Training needs no manual labels. The profiler is trained first. Records are then scored by how much their resulting profiles help prediction, and the query generator learns to fetch high-scoring ones. The approach beats existing methods across several predictors and also improves results on OpinionQA and PRISM. The findings suggest useful profiles must adapt to both context and predictor.

Key points

  • On Llama-3.3-70B-Instruct as predictor, F1 for view-change prediction climbs from 33% to 47%. Inference costs 6–13× less per instance than baselines that summarize the full history.
  • Data is split 8:1:1 by original poster to avoid leakage. User histories average 784 records per post (up to 19K). For training and validation, they are cut to at most 100 using a hybrid BM25 + BGE-M3 retriever.
  • Profile analysis covers five dimensions: Values & Ideologies, Emotional, Cognitive, Personality Traits, and Interests & Knowledge. No dimension helps on every post. For example, cognitive traits help on political posts but hurt on sociomoral ones.
  • Predictors (GPT-4o-mini, Llama-3.1-8B-Instruct, Llama-3.3-70B-Instruct) disagree on which records are useful. Top-5 overlap is only 0.24–0.28, and Spearman ρ is near zero. This motivates predictor-specific retrievers.
  • Low-utility records often share the post's topic or claim type, so surface similarity is a poor guide. Code and data are available at github.com/holi-lab/ReCAP.

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

🎓Seoul National UniversitySeoul · South Korea41 works · 45,671 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

Cite

Cite as Sejun Park, Yoonah Park, Jongwon Lim and Yohan Jo. 2026. Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.858

@inproceedings{park2026learning,
  title = {{Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction}},
  author = {Sejun Park, Yoonah Park, Jongwon Lim and Yohan Jo},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.858},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.858}
}

References

29 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Khalid Al Khatib, Michael Völske, Shahbaz Syed, Nikolay Kolyada, and Benno Stein. 2020. Exploiting personal characteristics of debaters for predicting persuasiveness. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7067–7072. find
  2. Esin Durmus and Claire Cardie. 2018. Exploring the role of prior beliefs for argument persuasion. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1035–1045, New Orleans, L… find
  3. Esin Durmus and Claire Cardie. 2019a. A corpus for modeling user and language effects in argumentation on online debating. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 602–607, Florence, Italy. Association for Computational Linguistics. find
  4. Esin Durmus and Claire Cardie. 2019b. Modeling the factors of user success in online debate. In The World Wide Web Conference (WWW) 2019, pages 2701– 2707. find
  5. Leandre R Fabrigar and Richard E Petty. 1999. The role of the affective and cognitive bases of attitudes in susceptibility to affectively and cognitively based persuasion. Personality and social psychology bulletin, 25(3):363–381. find
  6. Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2023. Precise zero-shot dense retrieval without relevance labels. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1762–1777, Toronto, Canada. Association for Computational Li…
  7. Kobi Hackenburg, Ben M Tappin, Luke Hewitt, Ed Saunders, Sid Black, Hause Lin, Catherine Fist, Helen Margetts, David G Rand, and Christopher Summerfield. 2025. The levers of political persuasion with conversational artificial intelligence. Science, 390(6777):eaea3884. find
  8. Christopher Hidey and Kathleen McKeown. 2018. Persuasive influence detection: The role of argument sequencing. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). find
  9. Christopher Hidey, Elena Musi, Alyssa Hwang, Smaranda Muresan, and Kathleen McKeown. 2017. Analyzing the semantic types of claims and premises in an online persuasive forum. In Proceedings of the 4th Workshop on Argument Mining, pages 11–21.
  10. Lu Ji, Zhongyu Wei, Xiangkun Hu, Yang Liu, Qi Zhang, and Xuanjing Huang. 2018. Incorporating argument-level interactions for persuasion comments evaluation using co-attention model. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3703–3714, Santa Fe, New Mexic… find
  11. Hannah R. Kirk, Alexander Whitefield, Paul Röttger, Andrew Bean, Katerina Margatina, Juan Ciro, Rafael Mosquera, Max Bartolo, Adina Williams, He He, Bertie Vidgen, and Scott A. Hale. 2024. The PRISM alignment dataset: What participatory, representative and individualised human feedback reveals about… find
  12. Cheng Li, Mingyang Zhang, Qiaozhu Mei, Weize Kong, and Michael Bendersky. 2024. Learning to rewrite prompts for personalized text generation. In Proceedings of the ACM Web Conference 2024, pages 3367–3378. find
  13. Jiwei Li, Michel Galley, Chris Brockett, Georgios Spithourakis, Jianfeng Gao, and Bill Dolan. 2016. A persona-based neural conversation model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 994–1003, Berlin, Germany. Associat…
  14. Stephanie Lukin, Pranav Anand, Marilyn Walker, and Steve Whittaker. 2017. Argument strength is in the eye of the beholder: Audience effects in persuasion. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 742–… find
  15. Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Bahareh Sarrafzadeh, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, and Tara Safavi. 2024. Pearl: Personalizing large language model writing assistants with generation-calibrated retrievers. In Proceedings of the 1st… find
  16. Richard M. Perloff. 2021. The Dynamics of Persuasion: Communication and Attitudes in the 21st Century, 7 edition. Routledge, New York, NY. find
  17. J Hunter Priniski and Zachary Horne. 2018. Attitude change on reddit’s change my view. In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 40. find
  18. Weicong Qin, Yi Xu, Weijie Yu, Teng Shi, Chenglei Shen, Ming He, Jianping Fan, Xiao Zhang, and Jun Xu. 2025. Similarity = value? consultation value-assessment and alignment for personalized search. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 9839–… find
  19. Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, and Abhinav Sethy. 2023. Integrating summarization and retrieval for enhanced personalization via large language models. arXiv preprint arXiv:2310.20081. — [arXiv:2310.20081](https://arxiv.org/abs/2310.20081) find
  20. Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. 2024b. Lamp: When large language models meet personalization. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7370–7392.
  21. Francesco Salvi, Manoel Horta Ribeiro, Riccardo Gallotti, and Robert West. 2024. On the conversational persuasiveness of large language models: A randomized controlled trial. arXiv preprint arXiv:2403.14380. — [arXiv:2403.14380](https://arxiv.org/abs/2403.14380) find
  22. Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. 2023. Whose opinions do language models reflect? In Proceedings of the 40th International Conference on Machine Learning, pages 29971–30004. PMLR.
  23. Chenhao Tan, Vlad Niculae, Cristian Danescu-Niculescu-Mizil, and Lillian Lee. 2016. Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions. In Proceedings of the 25th International Conference on World Wide Web (WWW), pages 613–624. find
  24. Qingyue Wang, Yanhe Fu, Yanan Cao, Shuai Wang, Zhiliang Tian, and Liang Ding. 2025. Recursively summarizing enables long-term dialogue memory in large language models. Neurocomputing, 639:130193. find
  25. Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He, and Tat-Seng Chua. 2025. Personalized generation in large model era: A survey. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), p…
  26. Jiarui Zhang. 2024. Guided profile generation improves personalization with large language models. In EMNLP (Findings). find
  27. Xinliang Frederick Zhang, Nick Beauchamp, and Lu Wang. 2025. Prime: Large language model personalization with cognitive dual-memory and personalized thought process. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 33695–33724. find
  28. Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. 2024. Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19724–19731. find
  29. Alireza Salemi, Surya Kallumadi, and Hamed Zamani. 2024a. Optimization methods for personalizing large language models through retrieval augmentation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 752–762. 17348 11 find

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