Journal article
How to Recommend by Online Lifestyle Tagging (OLT)
International journal of information technology & decision making, v 13(6), pp 1183-1209
01 Nov 2014
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 er 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 er 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.
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Details
- Title
- How to Recommend by Online Lifestyle Tagging (OLT)
- Creators
- Yu Pan - Beijing University of Posts and TelecommunicationsLijuan Luo - Beijing University of Posts and TelecommunicationsDan Liu - Beijing University of Posts and TelecommunicationsLi Gao - Shanghai International Studies UniversityXiaobo Xu - Shanghai Jiao Tong UniversityWenjing Shen - Drexel UniversityJiang Gao - Internet Business Department, China Tietong Telecommunications Corporation, Beijing 100055, China
- Publication Details
- International journal of information technology & decision making, v 13(6), pp 1183-1209
- Publisher
- World Scientific
- Number of pages
- 27
- Grant note
- 2013114ZD001 / Major Research Program of Shanghai International Studies University 20120005120001 / Ministry of Education of the People's Republic of China; Ministry of Education, China 2012CB315805 / National Basic Research Program (973 Program); National Basic Research Program of China 71201011; 71301106; 71103021; 71231002; 71172135; 71271099; 71471019 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000346223300005
- Scopus ID
- 2-s2.0-84929325064
- Other Identifier
- 991019167635804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Operations Research & Management Science