Joint Content semantic relation Learning with Mamba for Multimodal Recommendation

Multimodal recommendation systems have received widespread attention in recent years. The existing methods mainly treat multimodal features as a supplement to collaborative ID features to build alignment representation. However, these methods only use the user-item collaborative relation and item-item latent relation to implicitly represent multimodal features without explicitly using multimodal content, which ignores the intrinsic semantic relations within different modal content and makes the model not pay insufficient attention to user preferences, causing not great recommendation performance. Considering this problem, we argue the complete fine-grained representation of multimodal content, which includes collaboration relation, latent relation, and semantic relation. To address this, this paper proposes the Joint Content Semantic relation learning with Mamba for Multimodal Recommendation (JCSMRec). First, we construct Semantic relation representation with Mamba (SR²M) Module to explore the inter-modal correlation and provide an excellent aligned explicit representation for multimodal content. Then, we design a User preference-aware Module, according to user preference to guide learning the representation granularity of different modalities in the latent relations. Then, we conduct a joint representation to align and fusion multimodal features, which use the cyclic KL loss to align different semantic spaces and alleviate two joint losses to let user preference guide the learning process of the SR²M Module. This design effectively avoids over-reliance on ID features and enables a more comprehensive learning of textual and visual semantic information suitable for recommendation. Our method has achieved good results on multiple datasets and can be effectively inserted into other recommendation methods to improve results.