基于图卷积与外积的协同过滤推荐模型.

The function of recommendation system is to help users actively finding personalized items that meet theirs preferences and recommend them to users. Collaborative filtering algorithm is a classic algorithm in recommender system, but it is limited by cold start of data and sparsity and has disadvantages such as poor interpretability and poor model generalization ability . This paper studied its shortcomings. By taking the original score matrix in the form of user-project bipartite graph as input, designing the figure convolution neural network as a variant of graph autoencoder, which was obtained by the latent vector of user and item by iteratively aggregating neighbor node information, and combining CNN, this paper proposed a recommendation algorithm based on convolution matrix decomposition to improve the interpretability of the model and generalization ability. And also solved the auxiliary information fusion the data sparseness, and made recommendation performance improved by 1.4% and 1.7% . It provides a new idea for recommendation direction based on graph neural network in the future. [ABSTRACT FROM AUTHOR]