Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation
<|reference_start|>Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation: 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.<|reference_end|>
@article{buchanan2024incorporating,
    title={Incorporating Classifier-Free Guidance in Diffusion Model-Based
  Recommendation},
    author={Noah Buchanan, Susan Gauch, Quan Mai},
    journal={arXiv preprint arXiv:2409.10494},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10494},
    primaryClass={cs.IR cs.CL}
}