Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation
This work addresses the challenge of enhancing recommendation accuracy, especially under data sparsity, for users of recommender systems, representing an incremental advancement by applying a known diffusion innovation to this domain.
The paper tackles the problem of improving recommender systems by incorporating classifier-free guidance into a diffusion-based model, resulting in performance improvements over state-of-the-art systems across most metrics on various datasets, particularly in sparse data scenarios.
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.