A Survey on Diffusion Models for Recommender Systems

TL;DR

This paper presents the first comprehensive survey on diffusion models for recommendation, and draws a bird's-eye view from the perspective of the whole pipeline in real-world recommender systems.

Abstract

While traditional recommendation techniques have made significant strides in the past decades, they still suffer from limited generalization performance caused by factors like inadequate collaborative signals, weak latent representations, and noisy data. In response, diffusion models (DMs) have emerged as promising solutions for recommender systems due to their robust generative capabilities, solid theoretical foundations, and improved training stability. To this end, in this paper, we present the first comprehensive survey on diffusion models for recommendation, and draw a bird's-eye view from the perspective of the whole pipeline in real-world recommender systems. We systematically categorize existing research works into three primary domains: (1) diffusion for data engineering & encoding, focusing on data augmentation and representation enhancement; (2) diffusion as recommender models, employing diffusion models to directly estimate user preferences and rank items; and (3) diffusion for content presentation, utilizing diffusion models to generate personalized content such as fashion and advertisement creatives. Our taxonomy highlights the unique strengths of diffusion models in capturing complex data distributions and generating high-quality, diverse samples that closely align with user preferences. We also summarize the core characteristics of the adapting diffusion models for recommendation, and further identify key areas for future exploration, which helps establish a roadmap for researchers and practitioners seeking to advance recommender systems through the innovative application of diffusion models. To further facilitate the research community of recommender systems based on diffusion models, we actively maintain a GitHub repository for papers and other related resources in this rising direction https://github.com/CHIANGEL/Awesome-Diffusion-for-RecSys.

Figures (6)

• Figure 1: (a) The development trends and representational works of recommendation methods from traditional collaborative filtering (CF) based methods to generative methods, i.e., autoencoder (AE), variational autoencoder (VAE), generative adversarial network (GAN), and diffusion model. (b) The cumulative paper count of diffusion-based recommendation methods according to the timeline. (c) The paper distribution of diffusion-based recommendation methods over venues, where the venue name is followed by the exact number of published papers.
• Figure 2: (a) The illustration of a deep learning based recommender system pipeline, which is characterized by three major stages: data engineering & encoding, recommendation model, and content presentation. (b) An overview of diffusion models for data analysis and generation via the diffusion-denoising process.
• Figure 3: The overall categorization of diffusion models for recommendation. We also list the research works of each category attached with the corresponding method name and reference.
• Figure 4: The illustration of the core characteristic (i.e., flexibility) of diffusion models when adapting them to the data engineering & encoding stage in recommender systems. Diffusion models are generally compatible with various upstream input data types and downstream recommendation models.
• Figure 5: The illustration of three key perspectives when adapting diffusion models as recommenders: (1) what to diffuse, (2)what is the guidance (optional), and (3) how to accelerate.