Diffusion Models for Recommender Systems: From Content Distribution To Content Creation

Recommender systems (RSs) have become essential for alleviating information overload and matching users with relevant content. Traditionally, RSs have focused on personalized content distribution, leveraging user interaction data and various features to rank and recommend existing items. Recently, diffusion models (DMs) have emerged as powerful generative paradigms, introducing new possibilities for RSs to not only enhance their performance for content distribution but also extend their capability boundaries to personalized content creation. On the one hand, DMs enhance the recommendation performance by mitigating challenges such as sparse user-item interactions, weak latent representations, and noisy data. On the other hand, DMs enable personalized content creation, transforming RSs from passive distributors into active generators of user-specific media assets, such as customized images, posters, and multimedia content. Given such a transformative paradigm shift, this survey provides a comprehensive review of the integration of diffusion models into recommender systems, exploring key methodologies, application scenarios, and their impact on recommendation effectiveness, diversity, and personalization. We categorize DM-based recommendation paradigms into content distribution and content creation, compare integration strategies, and discuss open challenges and future directions. By bridging the gap between diffusion models and recommender systems, this work aims to guide researchers and practitioners in developing the next generation of generative AI-powered recommendation solutions.