Elamur Research⌕
☰
home / papers / 2026 / PrePPER: A Preference Pattern-based Profiling Framework for Explainable Recommendation

PrePPER: A Preference Pattern-based Profiling Framework for Explainable Recommendation

Summary

LLM-based recommenders that rely on natural-language user profiles are more transparent and easier for users to edit, but the authors identify two weaknesses in existing systems such as UPR and EXP3RT. Profiles do not indicate which preferences matter most to a user, and the aspects described in user profiles often fail to match those in item profiles. For example, a user profile may mention character development while the item profile only discusses reading experience. PrePPER, developed at NTT's Software Innovation Center, addresses both problems in four stages. It extracts preferences from user reviews, clusters liked and disliked preferences separately, and infers a characteristic "preference pattern" for each cluster. It then builds user and item profiles organised around these shared patterns, weighting each pattern by how many of a user's or item's preferences fall into its cluster. Because users and items are described along the same aspects with explicit importance, the profiles align better and can be plugged into existing profile-based recommenders. The authors report that this improves recommendation performance without redesigning the underlying systems.

Key points

  • Pipeline:
    1. Extract preferences from reviews, following the EXP3RT method, in "Like"/"Dislike" form.
    2. Cluster positive and negative preferences separately.
    3. Infer a preference pattern for each cluster.
    4. Construct profiles from the weighted patterns.
  • Importance weighting: The weight of each pattern is computed per user and per item from the number of their preference elements in the corresponding cluster.
  • Baselines and motivation: The work targets limitations of UPR (Ramos et al., 2024) and EXP3RT (Kim et al., 2025). It contrasts with ID-based explainable recommenders such as P5 and PEPLER-style prompt learning, which adapt poorly when user preferences change.
  • Integration: The profiles are presented as a drop-in component for existing profile-based LLM recommenders, keeping explanations interpretable and profiles editable by users.
  • Missing evaluation details: The provided excerpt omits the experimental section, so datasets, metrics and exact performance numbers could not be verified. The paper appears in LREC 2026, pp. 705–715.

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

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/45ibtz44yq3h

@inproceedings{usumi2026prepper,
  title = {{PrePPER: A Preference Pattern-based Profiling Framework for Explainable Recommendation}},
  author = {Usumi, Taisuke and Masaki, Akiko and Muramatsu, Sanae and Sakamoto, Akira and Eda, Takeharu},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/45ibtz44yq3h},
  url = {https://doi.org/10.63317/45ibtz44yq3h}
}

References

25 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774. find our card
  2. David Arthur and Sergei Vassilvitskii. 2007. kmeans++: the advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’07, page 1027–1035, USA. Society for Industrial and Applied Mathematics. find
  3. Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys ’23, page 1007–1014, New York, NY, USA.… find
  4. Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu. 2023. Uncovering chatgpt’s capabilities in recommender systems. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys ’23, page 1126–1132, New York, NY, USA. Association fo… find
  5. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. Qlora: efficient finetuning of quantized llms. In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS ’23, Red Hook, NY, USA. Curran Associates Inc. find
  6. Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023. Chat-rec: Towards interactive and explainable llms-augmented recommender system. arXiv preprint arXiv:2303.14524. — [arXiv:2303.14524](https://arxiv.org/abs/2303.14524) find
  7. Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022. Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems, RecSys ’22, page 299–315, New York, NY, USA. A… find
  8. Yupeng Hou, Jiacheng Li, Zhankui He, An Yan, Xiusi Chen, and Julian McAuley. 2024a. Bridging language and items for retrieval and recommendation. arXiv preprint arXiv:2403.03952. — [arXiv:2403.03952](https://arxiv.org/abs/2403.03952) find
  9. Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024b. Large language models are zero-shot rankers for recommender systems. In Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2… find
  10. Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang. 2024. Genrec: Large language model for generative recommendation. In Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedi… find
  11. Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023. Do llms understand user preferences? evaluating llms on user rating prediction. arXiv preprint arXiv:2305.06474. — [arXiv:2305.06474](https://arxiv.org/abs/2305.06474) find
  12. Jieyong Kim, Hyunseo Kim, Hyunjin Cho, SeongKu Kang, Buru Chang, Jinyoung Yeo, and Dongha Lee. 2025. Review-driven personalized preference reasoning with large language models for recommendation. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information… find
  13. Minjin Kim, Minju Kim, Hana Kim, Beong-woo Kwak, SeongKu Kang, Youngjae Yu, Jinyoung Yeo, and Dongha Lee. 2024. Pearl: A review-driven persona-knowledge grounded conversational recommendation dataset. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1105–1120, Bangkok, T… find
  14. Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37. find
  15. Lei Li, Yongfeng Zhang, and Li Chen. 2023. Personalized prompt learning for explainable recommendation. ACM Trans. Inf. Syst., 41(4). find
  16. Yueqing Liang, Liangwei Yang, Chen Wang, Xiongxiao Xu, Philip S. Yu, and Kai Shu. 2025. Taxonomy-guided zero-shot recommendations with LLMs. In Proceedings of the 31st International Conference on Computational Linguistics, pages 1520–1530, Abu Dhabi, UAE. Association for Computational Linguistics. find
  17. Junling Liu, Chao Liu, Peilin Zhou, Renjie Lv, Kang Zhou, and Yan Zhang. 2023. Is chatgpt a good recommender? a preliminary study. arXiv preprint arXiv:2304.10149. — [arXiv:2304.10149](https://arxiv.org/abs/2304.10149) find
  18. Zheng Liu, Yujia Zhou, Yutao Zhu, Jianxun Lian, Chaozhuo Li, Zhicheng Dou, Defu Lian, and Jian-Yun Nie. 2024. Information retrieval meets large language models. In Companion Proceedings of the ACM Web Conference 2024, WWW ’24, page 1586–1589, New York, NY, USA. Association for Computing Machinery. find
  19. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(1). find
  20. Jerome Ramos, Hossein A. Rahmani, Xi Wang, Xiao Fu, and Aldo Lipani. 2024. Transparent and scrutable recommendations using natural language user profiles. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13971–13984, Bangkok, T… find
  21. Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982–3992,… find
  22. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. CoRR, ab… find
  23. Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, and Enhong Chen. 2024. A survey on large language models for recommendation. World Wide Web, 27(5). find
  24. Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang, and Xiangnan He. 2025. Collm: Integrating collaborative embeddings into large language models for recommendation. IEEE Transactions on Knowledge and Data Engineering, 37(5):2329–2340. find
  25. Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zheng Liu, Zhicheng Dou, and Ji-Rong Wen. 2023. Large language models for information retrieval: A survey. arXiv preprint arXiv:2308.07107. find our card

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/usumi-2026-prepper-preference-pattern-based-profiling/