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Foundation Models for Recommender Systems: A Survey and New Perspectives

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arxiv 2402.11143 v1 pith:4D62BRKG submitted 2024-02-17 cs.IR

Foundation Models for Recommender Systems: A Survey and New Perspectives

Chengkai Huang , Tong Yu , Kaige Xie , Shuai Zhang , Lina Yao , Julian McAuley This is my paper
classification cs.IR
keywords fm4recsysresearchmodelssystemscharacteristicsfoundationopportunitiesrecommender
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Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs). In this paper, we attempt to thoroughly examine FM-based recommendation systems (FM4RecSys). We start by reviewing the research background of FM4RecSys. Then, we provide a systematic taxonomy of existing FM4RecSys research works, which can be divided into four different parts including data characteristics, representation learning, model type, and downstream tasks. Within each part, we review the key recent research developments, outlining the representative models and discussing their characteristics. Moreover, we elaborate on the open problems and opportunities of FM4RecSys aiming to shed light on future research directions in this area. In conclusion, we recap our findings and discuss the emerging trends in this field.

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