Large Language Models for Recommendation: Progresses and Future Directions
Keqin Bao, Jizhi Zhang, Yang Zhang, Wang Wenjie, Fuli Feng, Xiangnan He
Abstract
The powerful large language models (LLMs) have played a pivotal role in advancing recommender systems. Recently, in both academia and industry, there has been a surge of interest in developing LLMs for recommendation, referred to as LLM4Rec. This includes endeavors like leveraging LLMs for generative item retrieval and ranking, as well as the exciting possibility of building universal LLMs for diverse open-ended recommendation tasks. These developments hold the potential to reshape the traditional recommender paradigm, paving the way for the next-generation recommender systems.
Topics & Concepts
Ranking (information retrieval)Recommender systemComputer scienceGenerative grammarGenerative modelData scienceArtificial intelligenceWorld Wide WebTopic ModelingRecommender Systems and TechniquesAdvanced Graph Neural Networks