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Sustainability 2018, 10(1), 147; doi:10.3390/su10010147

Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business

1
College of Management Science, Chengdu University of Technology, Chengdu 610059, China
2
College of Architecture Engineering, Chengdu Aeronautic Polytechnic, Chengdu 610100, China
3
College of Public Management, China University of Mining and Technology, Xuzhou 221116, China
4
Department of Economic and Management, Sichuan Technology Business College, Chengdu 611830, China
*
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 21 December 2017 / Accepted: 7 January 2018 / Published: 9 January 2018
(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)
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Abstract

A lack of in-depth excavation of user and resources information has become the main bottleneck restricting the predictive analytics of recommendation systems in mobile commerce. This article provides a method which makes use of multi-source information to analyze consumers’ requirements for e-commerce recommendation systems. Combined with the characteristics of mobile e-commerce, this method employs an improved radial basis function (RBF) network in order to determine the weights of recommendations, and an improved Dempster–Shafer theory to fuse the multi-source information. Power-spectrum estimation is then used to handle the fusion results and allow decision-making. The experimental results illustrate that the traditional method is inferior to the proposed approach in terms of recommendation accuracy, simplicity, coverage rate and recall rate. These achievements can further improve recommendation systems, and promote the sustainable development of e-business. View Full-Text
Keywords: mobile e-commerce; information system; predictive analytics; information technology; data-mining; decision-making mobile e-commerce; information system; predictive analytics; information technology; data-mining; decision-making
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Guo, Y.; Yin, C.; Li, M.; Ren, X.; Liu, P. Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business. Sustainability 2018, 10, 147.

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