Work

SEALR: Sequential Emotion-Aware LLM-Based Personalized Recommendation System

Year: 2025

Type: conference-paper

Abstract: Large Language Models (LLMs) excel in various NLP tasks but remain underexplored in recommendation systems. This study proposes the Sequential Emotion-Aware LLM-Based Personalized Recommendation System (SEALR ) to leverage sentiment analysis in user-generated reviews, tracking emotional changes and extracting sentiment labels. It integrates candidate items produced by sequential models with user behavior data into an LLM, enhancing personalization. Experiments on Amazon and Yelp datasets explore the effect of varied candidate pool sizes and instruction-based fine-tuning ratios, demonstrating significant performance gains. The combination of sentiment insights and user behavior data effectively accommodates diverse user preferences and contexts.

Source: International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

DOI: 10.1145/3726302.3730249

Authors: Namjun Lee, Jaekwang Kim

Institution: Sungkyunkwan University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Research Foundation, Sungkyunkwan University, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion

Awards: G4288452366, G6500653232