title: LLM-based User Profile Management for Recommender System
summary: 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.
paper_authors._id: 67b8439499159e6fc939970b
paper_authors.name: Seunghwan Bang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T09:49:31.964Z
paper_authors._id: 67b841b821956199df64926b
paper_authors.avatarUrl: /avatars/e714df967ca1b78425fa188b6843f057.svg
paper_authors.fullname: Seunghwan Bang
paper_authors.type: user
paper_authors.user: Breadbang
paper_authors._id: 67b8439499159e6fc939970c
paper_authors.name: Hwanjun Song
paper_title: LLM-based User Profile Management for Recommender System
paper_summary: 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.
paper_ai_summary: PURE, a LLM-based recommendation framework, enhances user profiles through review analysis and generates personalized recommendations, outperforming existing LLM methods and managing token limitations.
paper_ai_keywords: Large Language Models LLM-based recommendation framework Review Extractor Profile Updater continuous sequential recommendation task token limitations