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Sensors 2016, 16(10), 1706; doi:10.3390/s16101706

An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments

1
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
2
College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China
3
Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
*
Author to whom correspondence should be addressed.
Academic Editors: M. Shamim Hossain and Athanasios V. Vasilakos
Received: 26 July 2016 / Revised: 20 September 2016 / Accepted: 9 October 2016 / Published: 15 October 2016
(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)
View Full-Text   |   Download PDF [4106 KB, uploaded 15 October 2016]   |  

Abstract

With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates. View Full-Text
Keywords: enhanced living environments; big data; recommendation filter model; smart home; Internet-of-Things enhanced living environments; big data; recommendation filter model; smart home; Internet-of-Things
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, H.; Xie, X.; Shu, W.; Xiong, N. An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments. Sensors 2016, 16, 1706.

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