Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks

Saisai Yu, Ming Guo (), Xiangyong Chen, Jianlong Qiu () and Jianqiang Sun
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Saisai Yu: School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
Ming Guo: School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
Xiangyong Chen: School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
Jianlong Qiu: School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
Jianqiang Sun: School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China

Mathematics, 2023, vol. 11, issue 6, 1-22

Abstract: With the rapid growth of the Internet, a wealth of movie resources are readily available on the major search engines. Still, it is unlikely that users will be able to find precisely the movies they are more interested in any time soon. Traditional recommendation algorithms, such as collaborative filtering recommendation algorithms only use the user’s rating information of the movie, without using the attribute information of the user and the movie, which has the problem of inaccurate recommendations. In order to achieve personalized accurate movie recommendations, a movie recommendation algorithm based on a multi-feature attention mechanism with deep neural networks and convolutional neural networks is proposed. In order to make the predicted movie ratings more accurate, user attribute information and movie attribute information are added, user network and movie network are presented to learn user features and movie features, respectively, and a feature attention mechanism is proposed so that different parts contribute differently to movie ratings. Text features are also extracted using convolutional neural networks, in which an attention mechanism is added to make the extracted text features more accurate, and finally, personalized movie accurate recommendations are achieved. The experimental results verify the effectiveness of the algorithm. The user attribute features and movie attribute features have a good effect on the rating, the feature attention mechanism makes the features distinguish the degree of importance to the rating, and the convolutional neural network adding the attention mechanism makes the extracted text features more effective and achieves high accuracy in MSE , MAE , MAPE , R 2 , and RMSE indexes.

Keywords: movie recommendation; attention mechanism; deep neural networks; convolutional neural networks; user network; movie network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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