ScienceDirect - Expert Systems with Applications : Personalization technology application to Internet content provider

Expert Systems with Applications
Volume 21, Issue 4, November 2001, Pages 203-215

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doi:10.1016/S0957-4174(01)00040-9

Personalization technology application to Internet content provider

Y. -F. Kuo ^,^,^a and L. -S. Chen ^b^,^c

^a Department of Industrial Management, Shu-Te University, 59, Hun Shan Road, Yen Chau, Kaohsiung 824, Taiwan ^b Graduate Institute of Management, National Yunlin University of Science and Technology, 123, University Road, Section 3, Touliu, Yunlin 640, Taiwan ^c Department of Information Management, Kun Shan University of Technology, 949, Da Wan Road, Yung Kang, Tainan 710, Taiwan

Available online 19 October 2001.

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Abstract

Personalization of Web pages relates closely to targeting the customer and increasing customer intimacy thereby leading to increased branding and, hopefully, consumer electronic commerce. Profiling individuals on the Web allows us not only to select which message to deliver to each individual, but also helps us learn about the needs and interests of each person. In this paper, a modified tracking analysis method to obtain user preference inclination was proposed and applied to simulating ICP (Internet content provider) Web site. SNT (browsing sequence, node, and time) data extracted from the simulating Web site database are used extensively for our analysis. The ability to track user browsing behavior down to individual mouse clicks has brought the vendor and end customer closer than ever before.

Author Keywords: Personalization technology; Internet content provider; Tracking analysis; Preference inclination; Data mining

Article Outline

1. Introduction
2. Literature review
3. Research method 3.1. Research process 3.1.1. Simulating Web site design 3.1.1.1. Decide Web site content category. 3.1.1.2. Decide Web site characteristics. 3.1.1.3. Web site framework combination. 3.1.1.4. Web site collection. 3.1.1.5. Web page analysis. 3.1.1.6. Web site construction. 3.2. Data collection 3.2.1. Record of browsing track 3.3. User preference analysis 3.3.1. Interest inclination analysis 3.3.1.1. Browsing sequence 3.3.1.2. Browsing node Browsing time where j is the category of Web page, j=0,1,…,9 (see Table 2); T_j is the total browsing time of Web page in category j. 3.3.2. Content characteristics presentation mode analysis 3.3.2.1. Browsing sequence 3.3.2.2. Browsing node 3.3.2.3. Browsing time
4. Numerical example 4.1. Interest inclination analysis 4.2. Content characteristics presentation mode analysis
5. Conclusions 5.1. Personalized strategy is helpful for management of ICP Web site 5.2. Importance of information category classification
Appendix A
References
Vitae

Expert Systems with Applications
Volume 21, Issue 4, November 2001, Pages 203-215

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