Diffusion Models in Recommendation Systems: A Survey

2025-01-17

AI Summary

Existing surveys lack a systematic taxonomy for diffusion models in recommender systems, often categorizing models by architectural roles (e.g., generator/encoder) rather than recommendation objectives. Method: This work introduces the first recommendation-oriented taxonomy, unifying foundational diffusion algorithms, collaborative filtering enhancement, sparse data modeling, and task-driven adaptation into a coherent framework; it is accompanied by an open-source GitHub knowledge base curating over 100 state-of-the-art papers. Contribution: It establishes the paradigm “diffusion serves recommendation goals,” offering a complementary analytical perspective to prior surveys; synthesizes key advances and recurring challenges; and identifies critical open problems—including dynamic interaction modeling, interpretability, and the efficiency–accuracy trade-off—to foster standardization and sustainable advancement in the field.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Recommender systems remain an essential topic due to its wide application in various domains and the business potential behind them. With the rise of deep learning, common solutions have leveraged neural networks to facilitate collaborative filtering, and some have turned to generative adversarial networks to augment the dataset and tackle the data sparsity issue. However, they are limited in learning the complex user and item distribution and still suffer from model collapse. 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. Diffusion models in recommender systems excel in managing complex user and item distributions and do not suffer from mode collapse. With these advantages, the amount of research in this domain have been growing rapidly and calling for a systematic survey. In this survey paper, we present and propose a taxonomy on past research papers in 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 foundation 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.

Problem

• Diffusion Models
• Recommendation Systems
• User-Item Relationships

Innovation

• Diffusion Models
• Recommendation Systems
• Complex User-Item Relationships