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