SemDiff: Semantic Guided Diffusion-Based Collaborative Filtering Framework

Diffusion models have demonstrated promising potential in recommender systems owing to their powerful generative ability. However, due to the inherent sparse nature of real-world recommendation data and the inconsistency in the variation of reconstruction and ranking losses during training, existing works suffer from two issues: 1) Randomly sampled Gaussian noise addition tends to obscure original user preferences. 2) Training for generation and preference learning tasks interferes with each other, limiting the generative ability of the model. To address these issues, we propose SemDiff, a semantic guided diffusion-based collaborative filtering framework. For the first issue, instead of using random Gaussian noise, we leverage rich semantic information by incorporating auxiliary signals from text or image modalities to enhance the input data of denoising model. In response to the second issue, based on a comprehensive analysis of the mutual influence between generation and preference learning in diffusion recommender systems, we build a collaborative training objective strategy to transform the interference between them into mutual collaboration, which jointly enhances the effectiveness of model training. Additionally, we employ multiple GCN layers only during inference to incorporate higher-order co-occurrence information while maintaining training efficiency. Extensive experiments on four real-world datasets demonstrate that SemDiff significantly outperforms state-of-the-art methods. Our SemDiff offers an effective solution for enhancing recommendation performance and suggests a novel paradigm for applying diffusion methods in recommender systems.