Personalized Movie Recommendation Method Based on Deep Learning

Abstract

With the rapid development of network technology and entertainment creation, the types of movies have become more and more diverse, which makes users wonder how to choose the type of movies. In order to improve the selection efficiency, recommend Algorithm came into being. Deep learning is a research field that has received extensive attention from scholars in recent years. Due to the characteristics of its deep architecture, deep learning models can learn more complex structures. Therefore, deep learning algorithms in speech recognition, machine translation, image recognition, and other fields have achieved impressive results. This article mainly introduces the research of personalized movie recommendation methods based on deep learning and intends to provide ideas and directions for the research of personalized movie recommendation under deep learning. This paper proposes a research method of personalized movie recommendation methods based on deep learning, including an overview of personalized recommendation and collaborative filtering recommendation algorithms, which are used to conduct research experiments on personalized movie recommendation methods based on deep learning. The experimental results in this paper show that the accuracy of the training set of the Seq2Seq model based on the LSTM recurrent neural network reaches 96.27% and the accuracy of the test set reaches 95.89%, which can be better for personalized movie recommendation.