Mining Users’ Preference Similarities in E-commerce Systems Based on Webpage Navigation Logs

  • Ping Li College of Biological and Environmental Sciences, Zhejiang Wanli University No. 8 South Qianhu Rd., Ningbo, Zhejiang, 315100, P. R. China,
  • Chunxue Wu School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology No. 516 Jun Gong Road, Shanghai 200093, P. R. China,
  • Shaozhong Zhang School of Electronic and Computer Science, Zhejiang Wanli University No. 8 South Qianhu Rd., Ningbo, Zhejiang, 315100, P. R. China,
  • Xinwu Yu The Information Center, Zhejiang Wanli University No. 8 South Qianhu Rd., Ningbo, Zhejiang, 315100, P. R. China,
  • Haidong Zhong Zhejiang Wanli University, Ningbo, Zhejiang, P.R.China


Mining users’ preference patterns in e-commerce systems is a fertile area for a great many application directions, such as shopping intention analysis, prediction and personalized recommendation. The web page navigation logs contain much potentially useful information, and provide opportunities for understanding the correlation between users’ browsing patterns and what they want to buy. In this article, we propose a web browsing history mining based user preference discovery method for e-commerce systems. First of all, a user-browsing-history-hierarchical-presentationgraph to established to model the web browsing histories of an individual in common e-commerce systems, and secondly an interested web page detection algorithm is designed to extract users’ preference. Finally, a new method called UPSAWBH (User Preference Similarity Calculation Algorithm Based on Web Browsing History), which measure the level of users’ preference similarity on the basis of their web page click patterns, is put forward. In the proposed UPSAWBH, we take two factors into account: 1) the number of shared web page click sequence, and 2) the property of the clicked web page that reflects users’ shopping preference in e-commerce systems. We conduct experiments on real dataset, which is extracted from the server of our self-developed e-commerce system. The results indicate a good effectiveness of the proposed approach.


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How to Cite
LI, Ping et al. Mining Users’ Preference Similarities in E-commerce Systems Based on Webpage Navigation Logs. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 12, n. 5, p. 661-676, sep. 2017. ISSN 1841-9844. Available at: <>. Date accessed: 03 aug. 2021. doi:


web browsing history mining, e-commerce, preference, recommendation. Copyright ©