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

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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