[Paper Review] A Survey on Diffusion Models for Recommender Systems

Jianghao Lin, Jiaqi Liu+6 more|arXiv (Cornell University)|Sep 8, 2024

Recommender Systems and Techniques4 citations

TL;DR

This survey provides the first comprehensive overview of diffusion models (DMs) in recommender systems, categorizing their applications into three domains: data engineering & encoding, direct recommendation modeling, and personalized content generation. It highlights DMs' superior generative capability, representation learning, and training stability, offering a roadmap for future research in this emerging field.

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.

Motivation & Objective

• To systematically categorize and analyze the application of diffusion models in recommender systems across three key domains: data engineering, recommendation modeling, and content presentation.
• To identify the core characteristics that make diffusion models effective for recommendation, including their generative capability, robust representation learning, and training stability.
• To highlight key challenges such as data privacy, digital copyright, and model interpretability in diffusion-based recommendation systems.
• To provide a structured research roadmap for future work by identifying underexplored directions, including integration with large language models and causal learning.
• To curate and maintain a public GitHub repository of resources to accelerate research in diffusion models for recommender systems.

Proposed method

• Proposes a three-tier taxonomy: (1) diffusion for data engineering & encoding (e.g., data augmentation, representation enhancement), (2) diffusion as recommendation models (e.g., direct preference modeling and ranking), and (3) diffusion for content presentation (e.g., generating fashion or ad creatives).
• Analyzes the denoising process of diffusion models as a key mechanism for learning complex data distributions and generating high-quality, diverse samples aligned with user preferences.
• Reviews existing methods that adapt diffusion models to handle sparse, noisy, or low-quality collaborative signals by learning disentangled and robust latent representations.
• Examines the integration of diffusion models with other paradigms such as large language models (LLMs) for explainable recommendations and causal learning for counterfactual reasoning.
• Discusses technical solutions to privacy and copyright issues, including neural watermarking, adversarial perturbations, federated learning, and differential privacy.
• Maintains a public GitHub repository to collect and organize papers and resources in the diffusion-based recommender systems domain.

Experimental results

Research questions

• RQ1 How can diffusion models be effectively applied to enhance data representation and augment user-item interaction data in recommender systems?
• RQ2 What are the key advantages of using diffusion models over traditional generative models (e.g., VAEs, GANs) in recommendation tasks?
• RQ3 In what ways can diffusion models be leveraged to generate personalized, high-quality content such as fashion designs or advertisement creatives?
• RQ4 How can diffusion models be integrated with large language models to improve recommendation explainability and user trust?
• RQ5 What are the major challenges related to digital copyright and data privacy in diffusion-based content generation, and how can they be mitigated?

Key findings

• Diffusion models demonstrate superior generative capability and representation learning compared to VAEs and GANs, enabling high-quality, diverse sample generation aligned with user preferences.
• The denoising framework of diffusion models ensures stable training and effective modeling of complex, multi-grained data distributions in recommendation systems.
• Applications in data engineering and encoding improve model generalization by enhancing latent representations and augmenting sparse interaction data.
• Diffusion-based recommendation models achieve state-of-the-art performance in tasks like top-N ranking and sequential recommendation by directly modeling user preference dynamics.
• In content presentation, diffusion models generate personalized, multi-modal creatives (e.g., fashion, ads) that significantly enhance user engagement and personalization.
• Integration with large language models enables the generation of contextually relevant, human-like justifications for recommendations, improving transparency and user satisfaction.