Personalized Movie Recommendation Method Based on Deep Learning

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