Combining User Contexts and User Opinions for Restaurant Recommendation in Mobile Environment

Combining User Contexts and User Opinions for Restaurant Recommendation in Mobile Environment

Qihua Liu (School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China) and Xiaohong Gan (School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China)
Copyright: © 2016 |Pages: 19
DOI: 10.4018/JECO.2016010105
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Abstract

In a mobile setting, user preferences vary in different contexts. Advances in mobile technologies have made the collection of user context information feasible, and as a result, the context-aware mobile recommender system field has been formed. Although there exist several different approaches to incorporating context into the recommendation process, context-aware recommendations are still difficult to compute. It is unclear which contextual factors are important and to which degree they influence user-item selection decisions. In this paper, the authors design a novel mobile restaurant recommender system combining user contexts and user opinions to provide restaurants to mobile users. This system extracts restaurant features from online user reviews and calculates the polarity of them based on sentiment analysis. It takes a new approach for assessing and modeling the relationship between contextual factors and restaurant features. On this basis, a hybrid recommender method is constructed, which integrates the contextual matching algorithm based on analytic hierarchy process and the collaborative filtering algorithm based on context similarity. From the user study, this system suggests restaurants that make the user more satisfied than another comparative system.
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Literature Review

Context-aware recommender systems have been recently explored in some fields, such as music (Reddy & Mascia, 2006; Hariri et al, 2012), tourism (Cena et al, 2006; Baltrunas et al, 2011; Levi et al, 2012; Yang et al, 2013; Tseng et al, 2013), mobile guides (Carmagnola et al, 2008), movies (Said et al, 2011), micro-blogging services and location-based social networks (Ma et al, 2011; Yuan et al, 2015), and advertising (Dao et al, 2012). In the study of context-aware recommender systems, obtaining context information and paradigms for incorporating context into recommender systems are the two key points (Adomavicius & Tuzhilin, 2011).

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