Abstract This paper proposes a system for finding a user's interests based on his browsing behaviors and the contents of his visited pages. An advanced client browser plug-in is implemented to track the user browsing behaviors and collect the information about the web pages that he has viewed. We develop a user-interest model in which user interests can be inferred by clustering and summarization the viewed page contents. The corresponding degree of his interest can be calculated based on his browsing behaviors and histories. The calculation for the interested degree is based on Gaussian process regression model which captures the relationship between a user's browsing behaviors and his interest to a web page. Experiments show that the system can find the user interests automatically and dynamically.
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References
A Library for Support Vector Machines (LIBSVM). http://www.csie.ntu.edu.tw/∼cjlin/ libsvm/
Atterer R, Wnuk M, and Schmidt A (2006) Knowing the User’s Every Move: User Activity Tracking for Website Usability Evaluation and Implicit Interaction. In Proceeding of the 15th International Conference on World Wide Web (Edinburgh Scotland, May 23-26, 2006). WWW’06, ACM Press, New York, pp 203-212
Cai D, Yu SP, Wen JR and Ma WY (2003) VIPS: a Vision-based Page Segmentation Algo- rithm. Microsoft Technical Report (MSR-TR-2003-79), November, 2003
S.M. Wild (2003) Seeding non-negative matrix factorizations with the spherical K-Means clustering. MS Thesis for the Department of Applied Mathematics, University of Colorado, April 2003
Lozano JA, Pena JM and Larranage, P (1999) An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognition Letters, 20: 1027-1040, 1999
Rasmussen CE and Williams CKI (2006) Gaussian Processes for Machine Learning, MIT Press, 2006
Weinreich H, Obendorf H, Herder E, and Mayer M (2006) Off the Beaten Tracks: Exploring Three Aspects of Web Navigation. In Proceeding of the 15th International Conference on World Wide Web (Edinburgh Scotland, May 23-26, 2006). WWW’06, ACM Press, New York, pp 133-142
White RW, and Drucker SM (2007). Investigating Behavioral Variability in Web Search. In Proceeding of the 16th International Conference on World Wide Web (Alberta Canada, May 8-12, 2007). WWW’07, ACM Press, New York, pp 21-30
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Li, F. et al. (2008). Combining Browsing Behaviors and Page Contents for Finding User Interests. In: Mahr, B., Huanye, S. (eds) Autonomous Systems – Self-Organization, Management, and Control. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8889-6_16
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DOI: https://doi.org/10.1007/978-1-4020-8889-6_16
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-8888-9
Online ISBN: 978-1-4020-8889-6
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