Abstract: Traditional collaborative filtering technology only used the user’s rating matrices on items to make recommendation. Because the rating matrices were too sparse and the traditional way did not take fully advantage of the many other features of users and objects, it led to a severe drop in recommendation accuracy for recommendation systems. In recent years, deep learning technology has made remarkable achievements in many fields of machine learning, in order to improve the traditional collaborative filtering recommendation system’s situation, this paper proposed a deep learning recommendation system based on dual attention-model of movie. This system used the depth learning framework to process multiple input feature information in Recommender systems, at the same time, which introduced dual attention mechanism and used the first attention layer to learn the user’s preference for film characteristics and the second attention layer to learn user’s preference for the complete movie in their watching list. After learning the user’s preference, the experimental results show that the recommendation performance has been improved.

Key words: dual attention model, deep learning, recommendation system, movie recommendation