Abstract: In recent years,graph network has been widely used in the field of recommendation and made a great progress.How-ever,the existing methods tend to focus on the interaction modeling of user projects,so the performance is limited by the problem of data sparsity.Therefore,this paper proposes a movie recommendation model based on graph attention network of attribute graph by using additional attribute information.Firstly,an attention-based GNN is proposed,which uses explicit feed-back to calculate the attention score between entities and attributes.Compared with the aggregation method using Laplace matrix,it can distinguish the influence of different attributes on entities more effectively,and the information aggregation between attributes and entities can be more effective.In addition,different entities are affected differently by attributes and behaviors,a fine-grained pre-ference fusion strategy is designed in this paper to calculate a set of preference fusion weights for each entity to make the embedding representation of entities more accurate and personalized.Experimental results on real data set show that the recommendation method that makes full use of attribute information contained in attribute graph can effectively alleviate the problem of data sparsity and is significantly better than other basic algorithm in terms of recall rate and nDCG,two evaluation indexes of movie recommendation.

Key words: Graph neural network, Attribute graph, Attention mechanism, Explicit feed-back, Movie recommendation