DualGNN:Dual Graph Neural Network For Multimedia Recommendation

In recent years,due to the rapid development of the short video industry,it is difficult for traditional graphic and text carriers to meet the increasingly rich content consumption needs of consumers,and micro video with multi-modal and intuitive information has gradually become the popular media form.The service providers are troubled by locating the interesting micro-videos for users.In order to solve this problem,they developed micro-video recommendation system to explore users’ preferences and rank candidate micro videos.Despite the remarkable performance of prior arts,they are still limited by fusing the user preference derived from different modalities in a unified manner,ignoring the users tend to place different emphasis on different modalities.Furthermore,modality-missing is ubiquity and unavoidable in the micro-video recommendation,some modalities information of micro-videos are lacked in many cases,which negatively affects the multi-modal fusion operations.To overcome these disadvantages,we propose a novel framework for the micro-video recommendation,dubbed Dual Graph Neural Network(DualGNN),upon the usermicrovideo bipartite and user co-occurrence graphs.Specifically,we first introduce a single-modal representation learning module,which performs graph operations on the user-microvideo graph in each modality to capture single-modal user preferences on different modalities.And then,we devise a multi-modal representation learning module to explicitly model the user’s attentions over different modalities and inductively learn the multi-modal user preference.Finally,we propose a prediction module to rank the potential micro-videos for users.Extensive experiments on two public datasets demonstrate the significant superiority of our DualGNN over state-of-the-arts methods.