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arXiv · 2409.10494

Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation

Noah Buchanan·Susan Gauch·Quan Mai
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Abstract

This paper presents a diffusion-based recommender system that incorporates classifier-free guidance. Most current recommender systems provide recommendations using conventional methods such as collaborative or content-based filtering. Diffusion is a new approach to generative AI that improves on previous generative AI approaches such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). We incorporate diffusion in a recommender system that mirrors the sequence users take when browsing and rating items. Although a few current recommender systems incorporate diffusion, they do not incorporate classifier-free guidance, a new innovation in diffusion models as a whole. In this paper, we present a diffusion recommender system that augments the underlying recommender system model for improved performance and also incorporates classifier-free guidance. Our findings show improvements over state-of-the-art recommender systems for most metrics for several recommendation tasks on a variety of datasets. In particular, our approach demonstrates the potential to provide better recommendations when data is sparse.

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BibTeXRIS

Noah Buchanan, Susan Gauch, Quan Mai. 2024-09-16. Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation. https://arxiv.org/abs/2409.10494

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