Video Recommendation Method Based on Deep Learning of Group Evaluation Behavior Sequences

Abstract

Massive video resources satisfy the interests of users on online video platforms but have led to the problem of the “explosion” of video resources. Meanwhile, some problems will also occur such as the sparse data, difficulty in extracting deep features and dynamic changes in user interests in video recommendation. Aiming at the problems, a video recommendation method is proposed based on the deep learning of group evaluation behavior. Using the Word2Vec word vector model, a video is mapped into a high-dimensional feature vector in an evaluation behavior sequence, a video feature vector library is generated, and a feature vector model of the video sequence is established. The convolutional neural networks (CNN), residual networks, and attention mechanisms are integrated to learn the deep connections between video feature vectors and to predict the candidate video sets. The candidate set is expanded by cosine similarity, and a dynamic interest model is established to filter and sort it. Experiments on the Movie-1M dataset show that this method can effectively improve the accuracy and recall rate of video recommendation, which verifies the feasibility and effectiveness of the method.