Abstract
The development of modern technology has made the movie recommendation system more and more diverse. However, it will also violate users’ privacy and lack the accuracy of recommendation information.This paper proposes a movie scoring algorithm based on deep neural networks. Firstly,user data is processed through homomorphic encryption. The pre-processed user data and movie data are embedded, and at the same time, the natural language text information of the movie name is embedded using word vectors. Then the text convolutional neural network is used to extract the local feature of the movie name vector sequence, and the feature is obtained after semantic fusion. Finally, the fully connected layer was used to jointly model user data and movie data to obtain the user's score prediction for the movie. This method was 0.237, 0.043, 0.057 lower than the user-based collaborative filtering algorithm, Slop one algorithm, and SVD++ algorithm on the MSE. Besides, after using the multi-scale convolution regression proposed in this paper, the MSE further decreases the rate by 0.027 based on the fully connected layer regression model.
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Acknowledgment
This work was supported by the High-Level Talent Project of the Natural Science Foundation of Hainan Province of China (grant number 2019RC117); Hainan Provincial Scientific Research Funding Projects for Colleges and Universities of China (Hnky2018-95); the National Key Research and Development Program of China (grant number 2016YFC0700804).
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Song, W., Fan, X., Li, J., Khan, A., Wang, L. (2021). Privacy-Preserving Movie Scoring Algorithm Based on Deep Neural Network. In: Cheng, J., Tang, X., Liu, X. (eds) Cyberspace Safety and Security. CSS 2020. Lecture Notes in Computer Science(), vol 12653. Springer, Cham. https://doi.org/10.1007/978-3-030-73671-2_24
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DOI: https://doi.org/10.1007/978-3-030-73671-2_24
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