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An improved ridge regression algorithm and its application in predicting TV ratings

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Abstract

Ridge regression is a biased estimated regressive method, which is traditionally used in collinearity data analysis. It is actually a modified Least Square method, which gains more rational and reliable regression coefficient by giving up the unbiasedness of Least Squares Estimation, reducing partial information and decreasing accuracy to overcome the over-fitting problems. This article presents an improved ridge regression algorithm and utilizes it to predict the audience rating for TV ratings. It is tested by 10 - fold Cross Validation. TV rating is an important indication to measure the quality and user experience, as well as one of the vital standards to state the value of a TV channel. The improved ridge regression algorithm is used to learn the model of weight matrix, which is trained by the error algorithm to predict the TV ratings. The extensive experimental results demonstrate the effectiveness of the proposed algorithm.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61672178, 61601458, 61701273 and 91420202) and the Project Funded by China Postdoctoral Science Foundation (No. 2017 M610897). The authors would also like to thank the anonymous reviewers for their constructive suggestions to improve the paper.

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Authors and Affiliations

  1. College of Robotics, Beijing Union University, Beijing, 100101, China

    Nan Ma & Xiuping Wu

  2. School of Software, Tsinghua University, Beijing, 100084, China

    Sicheng Zhao

  3. College of Information Technology, Beijing Union University, Beijing, 100101, China

    Zhen Sun

  4. E-Government Research Center, Chinese Academy of Governance, Beijincpg, 100089, China

    Yun Zhai

Authors
  1. Nan Ma
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  2. Sicheng Zhao
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  3. Zhen Sun
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  4. Xiuping Wu
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  5. Yun Zhai
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Corresponding author

Correspondence to Sicheng Zhao.

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Ma, N., Zhao, S., Sun, Z. et al. An improved ridge regression algorithm and its application in predicting TV ratings. Multimed Tools Appl 78, 525–536 (2019). https://doi.org/10.1007/s11042-017-5250-4

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  • DOI: https://doi.org/10.1007/s11042-017-5250-4

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