  As one of the main solutions to the information overload problem, recommender
systems are widely used in daily life. In the recent emerging micro-video
recommendation scenario, micro-videos contain rich multimedia information,
involving text, image, video and other multimodal data, and these rich
multimodal information conceals users' deep interest in the items. Most of the
current recommendation algorithms based on multimodal data use multimodal
information to expand the information on the item side, but ignore the
different preferences of users for different modal information, and lack the
fine-grained mining of the internal connection of multimodal information. To
investigate 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 the
network structure, fully explores the deep interest of users, measures the
importance of each dimension of user and item feature representation in the
scoring prediction task, makes the application of graph neural network in the
recommendation system is improved by using an attention mechanism to fuse the
multi-layer state output information, allowing the shallow structural features
provided by the intermediate layer to better participate in the prediction
task. The recommendation accuracy is improved compared with the traditional
recommendation algorithm on different data sets, and the feasibility and
effectiveness of our model is verified.
