title: An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments.
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.
authors.affiliation: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China. chenhao@hnu.edu.cn.
authors.forename: Hao
authors.initials: H
authors.lastname: Chen
authors.affiliation: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China. samxcode@hotmail.com.
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Xie
authors.affiliation: College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China. shuwanneng@whu.edu.cn.
authors.forename: Wanneng
authors.initials: W
authors.lastname: Shu
authors.affiliation: Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA. dnxiong@ieee.org.
authors.forename: Naixue
authors.initials: N
authors.lastname: Xiong
mesh_terms: D001185:Artificial Intelligence; D015374:Biosensing Techniques; D000067917:Cloud Computing; D004867:Equipment Design; D020407:Internet; D059015:Wireless Technology
keywords: Internet-of-Things; big data; enhanced living environments; recommendation filter model; smart home