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A user behavior prediction model based on parallel neural network and k-nearest neighbor algorithms

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Abstract

In the last decade, we have witnessed the dramatic development of the smart home industry. Smart home systems are currently facing an explosive growth of data. Making good use of this vast amount of data has become an attractive research topic in recent years. In order to develop smart home systems’ abilities for learning users’ behaviors autonomously and offering services spontaneously, a user behavior prediction model based on parallel back propagation neural network (BPNN) and k-nearest neighbor (KNN) algorithms is introduced in this paper. Based on MapReduce, a parallel BPNN algorithm is proposed to improve the prediction accuracy and speed, and a parallel KNN algorithm is developed for user decision-making rule selection. The experimental results indicate that the proposed model is significantly better than traditional user behavior prediction models in term of prediction accuracy and speed. A case study on smart home also illustrates the effectiveness of the proposed model.

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Acknowledgements

The research work presented in this paper is partially supported by the National Science Foundation of China (NSFC) (Grants Nos. 61573257, 71690234).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, China

    Gaowei Xu, Min Liu & Feng Zhang

  2. Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, ON, Canada

    Carl Shen

  3. The Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, China

    Weiming Shen

Authors
  1. Gaowei Xu
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  2. Carl Shen
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  3. Min Liu
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  4. Feng Zhang
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  5. Weiming Shen
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Correspondence to Min Liu.

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Xu, G., Shen, C., Liu, M. et al. A user behavior prediction model based on parallel neural network and k-nearest neighbor algorithms. Cluster Comput 20, 1703–1715 (2017). https://doi.org/10.1007/s10586-017-0749-z

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  • DOI: https://doi.org/10.1007/s10586-017-0749-z

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