Recommender systems remain an essential topic due to its wide application and business potential. Given the great generation capability exhibited by diffusion models in computer vision recently, many recommender systems have adopted diffusion models and found improvements in performance for various tasks. Research in this domain has been growing rapidly and calling for a systematic survey. In this survey paper, we propose and present a taxonomy based on three orthogonal axes to categorize recommender systems that utilize diffusion models. Distinct from a prior survey paper that categorizes based on the role of the diffusion model, we categorize based on the recommendation task at hand. The decision originates from the rationale that after all, the adoption of diffusion models is to enhance the recommendation performance, not vice versa: adapting the recommendation task to enable diffusion models. Nonetheless, we offer a unique perspective for diffusion models in recommender systems complementary to existing surveys. We present the foundational algorithms in diffusion models and their applications in recommender systems to summarize the rapid development in this field. Finally, we discuss open research directions to prepare and encourage further efforts to advance the field. We compile the relevant papers in a public GitHub repository.

翻译：推荐系统因其广泛应用和商业潜力，始终是一个重要的研究领域。鉴于扩散模型近期在计算机视觉领域展现出的强大生成能力，许多推荐系统已采用扩散模型，并在各类任务中实现了性能提升。该领域的研究正快速增长，亟需系统性的综述。本文提出并基于三个正交维度构建分类体系，对采用扩散模型的推荐系统进行归类。与先前基于扩散模型角色进行分类的综述不同，本文依据具体推荐任务进行分类。这一分类原则源于以下核心理念：采用扩散模型的根本目的是提升推荐性能，而非反之——通过调整推荐任务来适配扩散模型。尽管如此，我们为推荐系统中的扩散模型研究提供了与现有综述互补的独特视角。本文系统阐述了扩散模型的基础算法及其在推荐系统中的应用，以总结该领域的快速发展。最后，我们探讨了开放研究方向，以促进和鼓励推动该领域进步的后续研究。相关论文已整理至公开的GitHub仓库中。