@inproceedings{usumi-etal-2026-prepper,
title = "{P}re{PPER}: A Preference Pattern-based Profiling Framework for Explainable Recommendation",
author = "Usumi, Taisuke and
Masaki, Akiko and
Muramatsu, Sanae and
Sakamoto, Akira and
Eda, Takeharu",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.52/",
doi = "10.63317/45ibtz44yq3h",
pages = "705--715",
abstract = "Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks, drawing increasing attention to their application in recommendation systems. In particular, recommendation systems using natural language-based user profiles have attracted attention for improving transparency and scrutability. However, existing methods fail to fully leverage the recommendation capabilities of LLMs due to the unspecified importance of user preferences within user profiles and unmatched preference types between user profiles and item profiles. To address these challenges, we propose PrePPER, a novel preference pattern-based profiling framework designed to explicitly capture the importance of user preferences and enhance the alignment between user profiles and item profiles. PrePPER enables the extraction of users' preference patterns, which denote characteristic tendencies in user preferences, and the determination of their importance by clustering users' preferences. Specifically, we first extract users' preferences from their reviews and perform clustering on the extracted preferences. Based on the clustered preferences, we then infer users' preference patterns along with their relative importance, and construct user and item profiles using this information. Our proposed profiles incorporate the importance of user preferences and enhance the relatedness between user and item profiles, thereby improving the recommendation performance of existing recommender systems."
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<abstract>Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks, drawing increasing attention to their application in recommendation systems. In particular, recommendation systems using natural language-based user profiles have attracted attention for improving transparency and scrutability. However, existing methods fail to fully leverage the recommendation capabilities of LLMs due to the unspecified importance of user preferences within user profiles and unmatched preference types between user profiles and item profiles. To address these challenges, we propose PrePPER, a novel preference pattern-based profiling framework designed to explicitly capture the importance of user preferences and enhance the alignment between user profiles and item profiles. PrePPER enables the extraction of users’ preference patterns, which denote characteristic tendencies in user preferences, and the determination of their importance by clustering users’ preferences. Specifically, we first extract users’ preferences from their reviews and perform clustering on the extracted preferences. Based on the clustered preferences, we then infer users’ preference patterns along with their relative importance, and construct user and item profiles using this information. Our proposed profiles incorporate the importance of user preferences and enhance the relatedness between user and item profiles, thereby improving the recommendation performance of existing recommender systems.</abstract>
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%0 Conference Proceedings
%T PrePPER: A Preference Pattern-based Profiling Framework for Explainable Recommendation
%A Usumi, Taisuke
%A Masaki, Akiko
%A Muramatsu, Sanae
%A Sakamoto, Akira
%A Eda, Takeharu
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F usumi-etal-2026-prepper
%X Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks, drawing increasing attention to their application in recommendation systems. In particular, recommendation systems using natural language-based user profiles have attracted attention for improving transparency and scrutability. However, existing methods fail to fully leverage the recommendation capabilities of LLMs due to the unspecified importance of user preferences within user profiles and unmatched preference types between user profiles and item profiles. To address these challenges, we propose PrePPER, a novel preference pattern-based profiling framework designed to explicitly capture the importance of user preferences and enhance the alignment between user profiles and item profiles. PrePPER enables the extraction of users’ preference patterns, which denote characteristic tendencies in user preferences, and the determination of their importance by clustering users’ preferences. Specifically, we first extract users’ preferences from their reviews and perform clustering on the extracted preferences. Based on the clustered preferences, we then infer users’ preference patterns along with their relative importance, and construct user and item profiles using this information. Our proposed profiles incorporate the importance of user preferences and enhance the relatedness between user and item profiles, thereby improving the recommendation performance of existing recommender systems.
%R 10.63317/45ibtz44yq3h
%U https://aclanthology.org/2026.lrec-1.52/
%U https://doi.org/10.63317/45ibtz44yq3h
%P 705-715
Markdown (Informal)
[PrePPER: A Preference Pattern-based Profiling Framework for Explainable Recommendation](https://aclanthology.org/2026.lrec-1.52/) (Usumi et al., LREC 2026)
ACL