Multimodal Recommendation Algorithm Based on Contrastive Learning and Semantic Enhancement
The multimodal data of items is typically introduced into recommendation algorithms as additional auxiliary information to enrich the representation features of users and items.How to effectively integrate the interaction information with multimodal information of users and items is a key issue to the research.Existing methods are still insufficient in feature fusion and semantic association modeling.Therefore,a multimodal recommendation algorithm based on contrastive learning and semantic enhancement is proposed from the perspective of feature fusion.Firstly,the graph neural network and attention mechanism are adopted to fully integrate collaborative features and multimodal features.Next,the semantic association structures within each modality are learned under the guidance of the interaction structure in collaborative information.Meanwhile,the contrastive learning paradigm is employed to capture cross-modal representation dependencies.A reliability factor is introduced into the contrastive loss to adaptively adjust the constraint strength of the multimodal features,consequently suppressing the influence of data noise.Finally,the aforementioned tasks are jointly optimized to generate recommendation results.Experimental results on four real datasets show that the proposed algorithm yields excellent performance.
NETL
NSTL
万方数据
