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Score Prediction Model Based on Neural Network

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Abstract

With the rapid development of Internet technology, the data information on the network has also increased in an unprecedented amount. However, the emergence of this phenomenon makes it difficult for network users to find information of great value to themselves in the massive data. Therefore, score prediction is very important. It is an urgent task to establish an effective and practical scoring prediction model. This paper took movie score prediction as the key content and established a movie score prediction model based on convolutional neural network. The experimental result that the ten-layer convolutional neural network had the best performance was obtained through experiments; therefore it was taken as the main research subject for prediction. It was found that the accuracy of the test set of the ten-layer convolutional neural network was always around 56%, the accuracy of training set increased with the increase of training times. It was compared with the traditional decision tree prediction model. The accuracy, mean square error and root mean square error of the convolutional neural network prediction model were 56.78%, 0.7896 and 0.889 respectively; the prediction values were close to the actual values. Therefore the prediction model based on convolutional neural network was better. This study provides a new mode for the prediction of movie score.

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Funding

This study was supported by the Western Project of the National Social Science Fund of China: study on the nesting mechanism of government behavior and corporate social responsibility (grant number: 15XGL009).

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Authors and Affiliations

  1. School of Management, Sichuan University of Science and Engineering, Zigong, Sichuan, China

    Yijun Chen, Lan Guo & Cong Zhang

Authors
  1. Yijun Chen
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  2. Lan Guo
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  3. Cong Zhang
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Corresponding author

Correspondence to Cong Zhang.

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The authors declare that they have no conflicts of interest.

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Yijun Chen, Guo, L. & Zhang, C. Score Prediction Model Based on Neural Network. Opt. Mem. Neural Networks 29, 37–43 (2020). https://doi.org/10.3103/S1060992X20010038

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  • DOI: https://doi.org/10.3103/S1060992X20010038

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