Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation

TL;DR

This paper introduces a diffusion-based recommender system that uses classifier-free guidance to enhance recommendation quality, especially in data-sparse scenarios, outperforming existing methods on multiple metrics.

Contribution

It presents a novel diffusion recommender system incorporating classifier-free guidance, a new innovation, to improve recommendation performance over state-of-the-art models.

Findings

Outperforms existing recommender systems on multiple metrics

Shows significant improvements in data-sparse scenarios

Demonstrates the effectiveness of classifier-free guidance in diffusion models

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.