Foundation Models for Recommender Systems: A Survey and New Perspectives

TL;DR

This paper surveys the use of foundation models in recommender systems, categorizing existing research, highlighting recent developments, and discussing future challenges and opportunities in the field.

Contribution

It provides a comprehensive taxonomy of FM-based recommender systems, reviews key research developments, and identifies open problems and future research directions.

Findings

Systematic taxonomy of FM4RecSys research

Review of recent models and their characteristics

Discussion of open challenges and future trends

Abstract

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.