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ZTE Communications ›› 2018, Vol. 16 ›› Issue (1): 52-60.DOI: 10.3969/j.issn.1673-5188.2018.01.009

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  • 收稿日期:2017-08-05 出版日期:2018-02-25 发布日期:2020-03-16

Behavior Targeting Based on Hierarchical Taxonomy Aggregation for Heterogeneous Online Shopping Applications

ZHANG Lifeng, ZHANG Chunhong, HU Zheng, TANG Xiaosheng   

  1. Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2017-08-05 Online:2018-02-25 Published:2020-03-16
  • About author:ZHANG Lifeng (zhanglifeng@bupt.edu.cn) is a postgraduate student at Beijing University of Posts and Telecommunications, China. His research interests include data mining and massively parallel processing of data.|ZHANG Chunhong (zhangch@bupt.edu.cn) is a lecture of School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, China. She received her Ph.D. degree in computer science, M.Eng. degree in information technology, B.Eng. degree in telecommunication engineering in 1993, 1996 and 2013 respectively. She was a visiting scholar at Illinois Institute of Technology, USA in 2015. Her research interests include data mining, natural language processing, and ubiquitous computing.|HU Zheng (huzheng@bupt.edu.cn) received his Ph.D. degree from Beijing University of Posts and Telecommunications, China in 2008. He is working in the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications. His current research interests lie in the user behavior modeling and analysis in mobile internet and social networks. He has published more than 30 papers and been granted more than 10 patents in related area.|TANG Xiaosheng (txs@bupt.edu.cn) received his Ph.D. degree from Beijing University of Posts and Telecommunications, China. He is working in the Beijing University of Posts and Telecommunications. His current research interests include user behavior modeling and analysis in mobile internet and social networks. He has published more than 20 papers and been granted more than 10 patents in related areas.

Abstract:

Behavior targeting (BT) based on individual web-browsing history has become more valuable in precision marketing for many companies through capturing users ’interest and preference. It is common in practice that the behavior data collected from different online shopping applications are inconsistent since they are labelled by different item taxonomy, where the same behavior could have different representations and therefore analysis confusion arises. To address this issue, we propose a semantic similarity based strategy to transform the heterogeneous behavior extracted from deep packet inspection (DPI) data of a telecommunication operator into a unique standard one. The Word Mover’s Distance algorithm is exploited to evaluate the semantic similarity of the distributed representations of two web-browsing histories. Moreover, the architecture of the behavior targeting platform on Hadoop is implemented, which is capable of processing data with size of PB level every day.

Key words: BT, online shopping application, DPI, Word Mover’s Distance;, hierarchical taxonomy