How to Recommend by Online Lifestyle Tagging (OLT)

Yu Pan (), Lijuan Luo (), Dan Liu (), Li Gao (), Xiaobo Xu (), Wenjing Shen () and Jiang Gao ()
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Yu Pan: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Lijuan Luo: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Dan Liu: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Li Gao: College of Business Administration, Shanghai International Studies University, Shanghai 200083, China
Xiaobo Xu: Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
Wenjing Shen: Lebow College of Business, Drexel University, Philadelphia, PA 19104, USA
Jiang Gao: Internet Business Department, China Tietong Telecommunications Corporation, Beijing 100055, China

International Journal of Information Technology & Decision Making (IJITDM), 2014, vol. 13, issue 06, 1183-1209

Abstract: With the rapid development of the Internet, the online shopping market expands constantly. Inspired by fierce competition and complex and diverse consumer demand, personalized recommendation has become an effective marketing tool for e-commerce enterprises. However, the existing recommendation methods based on online consumer behavior or preferences are characterized by poor accuracy and low efficiency. The paper mainly conducts three studies, the study1 proves that seven online lifestyles, which are "Comfortable, Entertainment, Luxury, Tradition & Conservation, Rational, Fashion Sense, and Social Activities", affect Chinese consumers' purchase. However, the different online lifestyles have different effects on purchase, thus the response rates of recommending. The study2 proposes a new personalized recommendation method "online lifestyle tagging (OLT)" based on online lifestyle and user behavior tags to identify online lifestyles. In the study3, the efficiency of OLT is tested and verified using data collected from enterprises, it suggests that OLT has a significantly higher response rate than traditional behavior-based methods. This study demonstrates that OLT improves the accuracy and efficiency of personalized recommendation, and thus contributes to the theory of personalized recommendation and marketing methods based on lifestyle.

Keywords: Online lifestyle; behavioral tagging; personalized recommendation (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1142/S0219622014500795

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