@inproceedings{ecea8fa7e1be4634a13f1b71d708a53f,
title = "SEALR: Sequential Emotion-Aware LLM-Based Personalized Recommendation System",
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.",
keywords = "Large Language Model, Personalized Recommendation, Recommendation, Sentiment Analysis",
author = "Namjun Lee and Jaekwang Kim",
note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025 ; Conference date: 13-07-2025 Through 18-07-2025",
year = "2025",
month = jul,
day = "13",
doi = "10.1145/3726302.3730249",
language = "English",
series = "SIGIR 2025 - Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery, Inc",
pages = "2906--2910",
booktitle = "SIGIR 2025 - Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval",
}