Movie Recommendation System Research Based on Improved DeepFM

In recent years, the application of deep learning in many fields has gradually increased, and the practice in the field of information recommendation has also achieved excellent consequence. DeepFM model is used to share input data between wide and deep parts, it does not need artificial feature engineering, which can handle the advantages of low and high-order features simultaneously. In this paper, the hidden layer of DeepFM model is improved to solve the problem of gradient vanishing and gradient explosion in deep neural network effectively, and the improved algorithm model is verified by experiments on the MovieLens dataset. The improved model achieved a 1.29% increase in AUC indicator, and reduced the deviation between the predicted value and the true value of the model to a certain extent. Practice has proved that the improved model solves the problem of sparse film user rating data in the movie recommendation scenario, the accuracy of movie recommendation has been improved indirectly.