Movie Recommendation Model of Neural Collaborative Filtering Based on Multi Attribute Fusion

Abstract

With the development of science and technology and information technology, “information over-load” makes it difficult for users to quickly and accurately obtain the content they want from a large number of movie resources, which has become a major constraint affecting user experience. There-fore, personalized film recommendation has become the focus of research in the new era. The un-precedented development of deep learning has achieved great success in many fields. In order to alleviate the problem of data sparsity, this paper integrates multiple attribute information into the feature representation of users and items to obtain a complete primary feature representation of users and items. In addition, attention mechanism is used to distinguish the importance of these attributes, and then two fusion strategies, splicing and outer product, are used to model the poten-tial features of user movie, and multi-layer perceptrons and convolutional neural networks are respectively used to fully learn the nonlinear interaction between users and movies. Experiments on the movie dataset MovieLens 1M show that compared with the traditional DeepCF model, the model proposed in this paper improves the hit rate by 4.19% and the normalized cumulative loss gain by 0.38% respectively.