Diffusion Models in Recommendation Systems: A Survey

TL;DR

This survey paper proposes and presents a taxonomy based on three orthogonal axes to categorize recommender systems that utilize diffusion models, and offers a unique perspective for diffusion models in recommender systems complementary to existing surveys.

Abstract

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.

Figures (13)

• Figure 1: Growing Trend of Diffusion Models in Recommender Systems by November 2025 (non-cumulative).
• Figure 2: Timeline of Earliest Publications by Taxonomy Subcategory.
• Figure 3: Three-axis taxonomy of diffusion models in recommendation systems.
• Figure 4: Structure of the Foundation. We structure the foundation of diffusion models in recommender systems into three topics: diffusion model frameworks (Sections \ref{['sec:diffusion']} and \ref{['sec:improvement']}), efficiency enhancement (Section \ref{['sec:efficiency']}), and conditional generation (Section \ref{['sec:conditional']}).
• Figure 5: Diffusion Models in Pixel Space and Latent Space.