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.

翻译：近期，基础模型凭借其广泛的知识库和复杂架构，为推荐系统领域带来了独特机遇。本文旨在系统梳理基于基础模型的推荐系统研究。首先回顾FM4RecSys的研究背景，继而构建现有研究的系统分类法，将其分为四个维度：数据特征、表示学习、模型类型与下游任务。针对每个维度，我们综述了关键前沿进展，梳理代表性模型并剖析其特性。此外，深入探讨了FM4RecSys面临的开放性难题与发展机遇，冀望为未来研究方向提供启示。最终总结研究发现，并探讨该领域的新兴趋势。