LLM-based User Profile Management for Recommender System

TL;DR

This paper introduces PURE, an LLM-based framework that enhances recommender systems by extracting and updating user profiles from reviews, leading to improved personalized recommendations in dynamic scenarios.

Contribution

PURE is a novel framework that systematically builds and updates user profiles from textual reviews, addressing token limitations and improving recommendation accuracy.

Findings

PURE outperforms existing LLM-based methods on Amazon datasets.

The framework effectively leverages long-term user information.

PURE demonstrates improved recommendation quality in dynamic review scenarios.

Abstract

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.