Large Language Models Augmented Rating Prediction in Recommender System

Abstract

Recently, large language models (LLMs) have demonstrated impressive capabilities and gained widespread applications. However, their direct application to recommendation tasks (e.g., rating prediction task) often falls short of optimal results due to a lack of understanding of collaborative information in recommendations. In this paper, we propose Large lAnguage Model Augmented Recommendation (LAMAR) framework to address this limitation. Instead of relying solely on LLMs, our framework combines their outputs with traditional recommendation models, leveraging both collaborative and semantic information. We further enhance the recommendation performance through an ensemble of diverse prompts and utilize LLMs to extract side information for augmenting traditional recommendation models. Empirical studies on real-world datasets demonstrate that LAMAR outperforms existing approaches, highlighting the benefits of leveraging LLMs in recommendation systems. Code is available at https: //github.com/sichunluo/LAMAR.