Flip is Better than Noise: Unbiased Interest Generation for Multimedia Recommendation

Generative diffusion model approaches have achieved remarkable success in multimodal recommendation by generating latent user interest interaction graphs. However, current diffusion methods based on Gaussian noise introduce uncertain interest bias noise. This noise not only disrupts the original user-item interaction bipartite graph structure but also undermines the model's ability to accurately capture user interest preferences. To address these challenges, we propose Unbiased Interest Generation for Multimodal Recommendation (GenRec). Our approach aims to generate valid latent user interests while non-invasively preserving the original interest graph structure. We innovatively introduce the Multi-Modal Interest Generaction Module. During the forward process, we simulate user interest state transitions using ''forward flipping.'' In the reverse stage, we generate binary interaction graphs following a Bernoulli distribution. To further mitigate the random uncertainty during the generation process, we design a Multi-Modal Interest Debiase Module. By constructing a multimodal interest clustering space and using user interest hashing, we correct and enhance the generated interest graphs. Finally, the Multi-modal High-Order Graph Learning Optimization is employed to capture high-order interaction information between users and items. A contrastive learning loss function is used for model optimization. We conduct extensive experiments on different real-world open datasets from the industrial sector. Compared with the state-of-the-art DiffMM, our method significantly improves NDCG@20 by 7.3% on the TikTok dataset and boosts Recall@20 by 4.2% on the Sports dataset. The experimental results validate the effectiveness of GenRec. The code is publicly available at https://github.com/orangeheyue/GenRec-V1.