Exploring the Influence of Contexts for Mobile Recommendation

Exploring the Influence of Contexts for Mobile Recommendation

Jun Zeng (Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University, Chongqing, China), Feng Li (Graduate School of Software Engineering, Chongqing University, Chongqing, China), Yinghua Li (Graduate School of Software Engineering, Chongqing University, Chongqing, China), Junhao Wen (Graduate School of Software Engineering, Chongqing University, Chongqing, China) and Yingbo Wu (Graduate School of Software Engineering, Chongqing University, Chongqing, China)
Copyright: © 2017 |Pages: 17
DOI: 10.4018/IJWSR.2017100102
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Abstract

With the rapid development of mobile internet, it is difficult to obtain high-quality recommendation in such a complicated mobile environment, just depending on traditional user-item binary information. How to use multiple contexts to generate satisfying recommendation has been a hot topic in some fields like e-commerce, tourism and news. Context aware recommender system (CARS) imports contexts into recommender to generate ubiquitous and personalized recommendation. In this paper, the basic information of CARS, such as the definition of context, the process of CARS and evaluation are introduced carefully. In order to explore whether contexts have a great influence on recommendation or not, the authors conduct experiments on real datasets. Experimental results show recommender that incorporates contexts significantly improves performance over the traditional recommender. Finally, State of the art about CARS is detailed.
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Mobile Recommender System

At present, mobile devices are widely used in people’ daily life. It has been able to replace traditional PC devices in some respects, which mainly reflects in two aspects: the continuous development of basic functions and the utilization of multi resources.

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