LETTER: Self-Harmonized Representation Learning for Multimodal Recommendation

Multimodal recommender systems try to integrate multimedia data (images, texts, etc.) with user-item historical records to better model user preference. However, most previous methods largely ignored the underlying fine-grained attribute features of items, which makes it difficult to fully explore users' nuanced attention across individual and combined attributes, resulting in low recommendation performance. To address these issues, this paper proposes a novel and effective self-harmonized representation learning network for multimodal recommendation, named LETTER. LETTER has the ability to effectively optimize the user and item representations for multimodal recommendation. Specifically, we design a factorized attribute interaction module that captures diverse combinations of item latent attributes using a bilinear pooling strategy. Then a dual graph convolution module is established to learn the modality-specific representations from user-item interactive and item semantic relations. Finally, we design a preference self-harmonization module that adaptively identifies the salient influencing factors of user preference, thus refining user and item representations to improve recommendation accuracy. We conduct extensive experiments on three real-world datasets, demonstrating that LETTER outperforms state-of-the-art multimodal recommendation methods.