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Cluster Computing
Erschienen in:

23.02.2017

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

verfasst von: Gaowei Xu, Carl Shen, Min Liu, Feng Zhang, Weiming Shen

Erschienen in: Cluster Computing | Ausgabe 2/2017

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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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Metadaten
Titel
A user behavior prediction model based on parallel neural network and k-nearest neighbor algorithms
verfasst von
Gaowei Xu
Carl Shen
Min Liu
Feng Zhang
Weiming Shen
Publikationsdatum
23.02.2017
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 2/2017
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-0749-z

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