Diffusion Models in Recommendation Systems: A Survey

TL;DR

This survey reviews the integration of diffusion models into recommender systems, categorizing their applications based on recommendation tasks and highlighting recent advancements and future research directions.

Contribution

It introduces a novel taxonomy for diffusion-based recommender systems based on recommendation tasks, offering a unique perspective different from prior surveys.

Findings

Diffusion models have been increasingly adopted in recommender systems.

A comprehensive taxonomy categorizes applications by recommendation tasks.

The survey highlights open research directions and provides a public repository of relevant papers.

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.