Mining Web navigation patterns with a path traversal graph |
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| Authors: | Yao-Te Wang Anthony J.T. Lee |
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| Affiliation: | 1. Department of Computer Science and Information Management, Providence University, 200 Chung-chi Road, Shalu, Taichung 433, Taiwan, ROC;2. Department of Information Management, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan, ROC;1. KAIST, 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon 305-701, Republic of Korea;2. Microsoft Research Asia, Tower 2, No. 5 Danling Street, Haidian District, Beijing 100080, PR China;1. Innovative Information Industry Research Center (IIIRC), School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, HIT Campus Shenzhen University Town Xili, Shenzhen, PR China;2. Shenzhen Key Laboratory of Internet Information Collaboration, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, HIT Campus Shenzhen University Town Xili, Shenzhen, PR China;3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC;4. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC;5. Department of Mathematics and Computer Sciences, Fuqing Branch of Fujian Normal University, Fuzhou, Fujian, PR China;1. Ministry of Education Key Laboratory for Earth System Modeling, and Center for Earth System Science, Tsinghua University, Beijing, China;2. Department of Computer Science and Technology, Tsinghua University, Beijing, China;3. Computer Science Division, University of California at Berkeley, Berkeley, CA, USA;4. College of Computing, Georgia Institute of Technology, Atlanta, GA, USA;1. Universität Bremen, PO Box 330 440, 283334 Bremen, Germany;2. University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, United Kingdom;1. University of Vienna, Faculty of Computer Science, 1090 Vienna, Austria;2. ETH Zurich, 8092 Zurich, Switzerland |
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| Abstract: | Understanding the navigational behaviour of website visitors is a significant factor of success in the emerging business models of electronic commerce and even mobile commerce. However, Web traversal patterns obtained by traditional Web usage mining approaches are ineffective for the content management of websites. They do not provide the big picture of the intentions of the visitors. The Web navigation patterns, termed throughout-surfing patterns (TSPs) as defined in this paper, are a superset of Web traversal patterns that effectively display the trends toward the next visited Web pages in a browsing session. TSPs are more expressive for understanding the purposes of website visitors. In this paper, we first introduce the concept of throughout-surfing patterns and then present an efficient method for mining the patterns. We propose a compact graph structure, termed a path traversal graph, to record information about the navigation paths of website visitors. The graph contains the frequent surfing paths that are required for mining TSPs. In addition, we devised a graph traverse algorithm based on the proposed graph structure to discover the TSPs. The experimental results show the proposed mining method is highly efficient to discover TSPs. |
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