Zhongtai Qin
ABSTRACT
In view of the limitations of existing personalized movie recommendation algorithms, in this article, we propose a personalized movie recommendation system based on deep neural networks. The system uses a deep neural network to process discrete features to fully dig out the various features between the user and the movie, and construct a user feature vector model and a movie feature vector model with demographic characteristics. Specifically, we introduce an attention mechanism into the model to learn the user's behavioral preferences and integrate user feature vectors to obtain the user's dynamic short-term interest features and static long-term historical interest features. This method enhances the recommendation performance of the model. We conduct performance exploration on the public dataset MovieLens, and the proposed recommendation model is superior to traditional recommendation algorithms in terms of accuracy and recall, and has higher recommendation performance.
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