LLM-based User Profile Management for Recommender System
This work addresses the need for more personalized and dynamic recommendations in e-commerce by leveraging textual data, though it is incremental as it builds on existing LLM-based approaches.
The paper tackles the problem of improving LLM-based recommender systems by incorporating user-generated textual data like reviews to build evolving user profiles, resulting in PURE outperforming existing methods on Amazon datasets in a continuous sequential recommendation task.
The rapid advancement of Large Language Models (LLMs) has opened new opportunities in recommender systems by enabling zero-shot recommendation without conventional training. Despite their potential, most existing works rely solely on users' purchase histories, leaving significant room for improvement by incorporating user-generated textual data, such as reviews and product descriptions. Addressing this gap, we propose PURE, a novel LLM-based recommendation framework that builds and maintains evolving user profiles by systematically extracting and summarizing key information from user reviews. PURE consists of three core components: a Review Extractor for identifying user preferences and key product features, a Profile Updater for refining and updating user profiles, and a Recommender for generating personalized recommendations using the most current profile. To evaluate PURE, we introduce a continuous sequential recommendation task that reflects real-world scenarios by adding reviews over time and updating predictions incrementally. Our experimental results on Amazon datasets demonstrate that PURE outperforms existing LLM-based methods, effectively leveraging long-term user information while managing token limitations.