A novel mobile recommender system for indoor shopping

Authors:
Bing Fang;Shaoyi Liao;Kaiquan Xu;Hao Cheng;Chen Zhu;Huaping Chen
Affiliations:
School of Management, University of Science and Technology of China, China and Department of Information Systems, City University of Hong Kong, Hong Kong;Department of Information Systems, City University of Hong Kong, Hong Kong and School of Economics & Management, Southwest Jiaotong University, China;School of Business, Nanjing University, China;School of Management, Hefei University of Technology, China;Department of Information Systems, City University of Hong Kong, Hong Kong and School of Economics & Management, Southwest Jiaotong University, China;School of Management, University of Science and Technology of China, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

With the widespread usage of mobile terminals, the mobile recommender system is proposed to improve recommendation performance, using positioning technologies. However, due to restrictions of existing positioning technologies, mobile recommender systems are still not being applied to indoor shopping, which continues to be the main shopping mode. In this paper, we develop a mobile recommender system for stores under the circumstance of indoor shopping, based on the proposed novel indoor mobile positioning approach by using received signal patterns of mobile phones, which can overcome the disadvantages of existing positioning technologies. Especially, the mobile recommender system can implicitly capture users' preferences by analyzing users' positions, without requiring users' explicit inputting, and take the contextual information into consideration when making recommendations. A comprehensive experimental evaluation shows the new proposed mobile recommender system achieves much better user satisfaction than the benchmark method, without losing obvious recommendation performances.