A Diffusion Model with User Preference Guidance for Recommendation

Boci Peng, Xiaohe Bo, Jiayan Guo, Yan Zhang

Abstract: With the merits of their distribution generation and diversity representation, diffusion models have achieved remarkable success in the field of recommendation. However, applying diffusion models for generating user interactions may carry the risk of erasing users’ personalized information, degrading the recommendation accuracy and the user satisfaction. Therefore, it becomes imperative to integrate additional information to guide the generation process of diffusion models. In light of this, we propose a conditional Diffusion model with User Preference guidance (DiffUP), which considers introducing user preference information into diffusion models for controllable generation. Firstly, we propose to construct step-aware noisy graphs under each reverse step and utilize the high-order user embeddings obtained from a holistic view. Furthermore, we design a cluster-based classifier-free guidance method, in which we cluster users and elaborate the class information as a preference signal. We also extend our method to the latent diffusion model, termed L-DiffUP. Extensive experiments on four real-world datasets demonstrate the superiority of our methods.