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Discovering user profiles for Web personalized recommendation

  • Knowledge and Data Processing
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Abstract

With the growing popularity of the World Wide Web, large volume of user access data has been gathered automatically by Web servers and stored in Web logs. Discovering and understanding user behavior patterns from log files can provide Web personalized recommendation services. In this paper, a novel clustering method is presented for log files called Clustering large Weblog based on Key Path Model (CWKPM), which is based on user browsing key path model, to get user behavior profiles. Compared with the previous Boolean model, key path model considers the major features of users' accessing to the Web: ordinal, contiguous and duplicate. Moreover, for clustering, it has fewer dimensions. The analysis and experiments show that CWKPM is an efficient and effective approach for clustering large and high-dimension Web logs.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Southeast University, 210096, Nanjing, P. R. China

    Ai-Bo Song, Zuo-Peng Liang, Yi-Sheng Dong & Jun-Zhou Luo

  2. Information School, Shandong University of Science and Technology, 271019, Taian, P.R. China

    Mao-Xian Zhao

Authors
  1. Ai-Bo Song
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  2. Mao-Xian Zhao
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  3. Zuo-Peng Liang
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  4. Yi-Sheng Dong
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  5. Jun-Zhou Luo
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Corresponding author

Correspondence to Ai-Bo Song.

Additional information

This work is supported by the Special Program “Network based Science Activity Environment” of the National Natural Science Foundation of China, and Jiangsu Provincial Key Laboratory of Network and Information Security under Grant No.BM2003201.

Ai-Bo Song received the B.S. and M.S. degrees from School of Information and Engineering, Shandong University of Science and Technology in 1993 and 1996, respectively. Currently he is a Ph.D. candidate in Department of Computer Science and Engineering at Southeast University. His current research interests include data mining, data warehousing and Petri nets.

Mao-Xian Zhao is a Ph.D. candidate in School of Transportation and Traffic at Northern Jiaotong University. His current research interests are optimization & its application and algorithm analysis.

Zuo-Peng Liang is a Ph.D. candidate in Department of Computer Science and Engineering at Southeast University. His current research interests include data mining and XML data management.

Yi-Sheng Dong received the B.S. degree from Department of Computer Science and Engineering, Southeast University in 1965. Since then, he has been with Southeast University. His main research interests are database and software technology.

Jun-Zhou Luo is a professor of Department of Computer Science and Engineering, Southeast University, the secretary-general Petri net Committee of China Computer Federation, an active member of Now York Academy of Science. His current research interests include Petri-nets-based protocol engineering, computer network, and concurrent engineering.

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Song, AB., Zhao, MX., Liang, ZP. et al. Discovering user profiles for Web personalized recommendation. J. Comput. Sci. & Technol. 19, 320–328 (2004). https://doi.org/10.1007/BF02944902

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  • DOI: https://doi.org/10.1007/BF02944902

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