Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation

• URL: http://arxiv.org/abs/2409.10494v1
• Date: Mon, 16 Sep 2024 17:27:27 GMT
• Title: Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation
• Authors: Noah Buchanan, Susan Gauch, Quan Mai,
• Abstract summary: Diffusion is a new approach to generative AI that improves on previous generative AI approaches. We incorporate diffusion in a recommender system that mirrors the sequence users take when browsing and rating items.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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