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As one of the main solutions to the information overload problem, recommendersystems are widely used in daily life. In the recent emerging micro-videorecommendation scenario, micro-videos contain rich multimedia information,involving text, image, video and other multimodal data, and these richmultimodal information conceals users' deep interest in the items. Most of thecurrent recommendation algorithms based on multimodal data use multimodalinformation to expand the information on the item side, but ignore thedifferent preferences of users for different modal information, and lack thefine-grained mining of the internal connection of multimodal information. Toinvestigate the problems in the micro-video recommendr system mentioned above,we design a hybrid recommendation model based on multimodal information,introduces multimodal information and user-side auxiliary information in thenetwork structure, fully explores the deep interest of users, measures theimportance of each dimension of user and item feature representation in thescoring prediction task, makes the application of graph neural network in therecommendation system is improved by using an attention mechanism to fuse themulti-layer state output information, allowing the shallow structural featuresprovided by the intermediate layer to better participate in the predictiontask. The recommendation accuracy is improved compared with the traditionalrecommendation algorithm on different data sets, and the feasibility andeffectiveness of our model is verified.

A multimedia recommendation model based on collaborative graph