M³Rec: Selective State Space Models with Mixture-of-Modality Experts for Multi-Modal Sequential Recommendation

The rapid growth of multimedia-sharing platforms drives the development of recommender systems. While traditional ID-based methods for mining user behavior signals are well-studied, research into multimodal sequential recommendation remains nascent. Current approaches face three critical challenges: (1) inadequate modeling of user preferences across diverse modalities, (2) ineffective capture of user action sequence dependencies hinders representation learning of preferences, and (3) inefficiency in Transformer-based models due to the quadratic complexity of attention mechanisms. To address these issues, we propose M³Rec, a Mamba-based selective state space model incorporating Mixture-of-Modality experts for Multimodal sequential recommendation. M³Rec strengthens the modeling of user action sequence dependencies through shared Mamba blocks across modalities and employs modality experts to extract modality-specific user preferences. The shared Mamba blocks efficiently model long-term user preferences with fast inference and linear scalability through hardware-aware parallelism, enhancing ID-based sequence signals and filtering out non-action-dependent redundant information. This enables more accurate modeling of user preferences across heterogeneous data. Extensive experiments on three public datasets validate the model's effectiveness. The implementation is released at https://github.com/Xu107/M3Rec-main.