A Diffusion-Based Sequential Recommender via Partial Noise Injection and Cross-Expert Comparison

Abstract

In recent years, diffusion models have demonstrated remarkable performance across various domains and have shown their potential in recommender systems. However, in sequential recommendation, diffusion models face inherent limitations: the mismatch between the diffusion mechanism and sequence modeling exacerbates the negative impact of random noise on user preferences. In addition, user sequence information is not fully leveraged, leading to insufficient guidance in the forward and reverse processes and hindering the generation of high-quality preference representations. In this article, we propose a diffusion-based sequential recommender incorporating expert comparison and constraints to address these challenges. Specifically, we introduce a partial noise injection method to mitigate the impact of noise and enable conditional sequence guidance. Furthermore, by designing a cross-expert comparison and constraint mechanism, our approach effectively captures diverse sequential preferences, ultimately generating high-quality item representations. Experimental results demonstrate that our model produces representations that accurately reflect users’ multi-faceted preferences and achieves superior recommendation performance compared to state-of-the-art methods.