Diffusion Models in Recommendation Systems: A Survey

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

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 complementary axes to categorize recommender systems that utilize diffusion models. Distinct from prior survey work that primarily categorizes based on the role of diffusion models, we organize the taxonomy primarily around the recommendation task. This choice is motivated by the observation that diffusion models are typically adopted to improve recommendation performance under different task settings. Our taxonomy provides a complementary perspective to existing surveys. We present the foundational algorithms in diffusion models and their applications in recommender systems to summarize recent progress in this field. Finally, we discuss open research directions to encourage further research. We compile the relevant papers in a public GitHub repository.