Multi-Interest Network with Simple Diffusion for Multi-Behavior Sequential Recommendation

Abstract

Multi-behavior sequential recommendation (MBSR) aims to learn dynamic user preference from historical heterogeneous user interactions for identifying the next item under target behavior (i.e., purchase). Although significant efforts have been devoted to modeling users over observed multi-behavior interaction sequences, user modeling with dynamic behavior-aware multiple interests and elimination of inherent noises within these interactions are still underexplored. This limits user representations' awareness of true preference evolution and further constrains recommendation performance. To address the aforementioned issues, we propose a Multi-Interest Network with Simple Diffusion (MISD) via a combination of multi-interest learning and diffusion generative process for MBSR. Concretely, the dynamic multi-interest network is proposed to generate time-evolving personalized interests from the encoded dual-granularity user sequential patterns, leading to more accurate user preference learning. Additionally, simple diffusion is proposed to model the complex latent preference generation procedures in an iterative denoising manner, thereby alleviating the effect of noisy interactions. Extensive experiments on three real-world datasets demonstrate that MISD consistently outperforms various state-of-the-art recommendation methods under multiple settings (e.g., clean and noisy training).