Foundation Models for Recommender Systems: A Survey and New Perspectives
CoRR(2024)
摘要
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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