Abstract
The amount of information on the Web is rapidly increasing. Recommender systems can help users selectively filter this information based on their preferences. One way to obtain user preferences is to analyze characteristics of content that is accessed by the user. Unfortunately, web pages may contain elements irrelevant to user interests (e.g., navigation bar, advertisements, and links.). Hence, existing analysis approaches using the TF-IDF method may not be suitable. This paper proposes a novel user preference analysis system that eliminates elements that repeatedly appear in web pages. It extracts user interest keywords in the identified primary content. Also, the system has features that collect the anchor tag, and track the user’s search route, in order to identify keywords that are of core interest to the user. This paper compares the proposed system with pure TF-IDF analysis method. The analysis confirms its effectiveness in terms of the accuracy of the analyzed user profiles.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Melamed, D., Shapira, B., Elovici, Y.: MarCol: A Market-Based Recommender System. IEEE Intelligent Systems 22(3), 74–78 (2007)
Aciar, S., Zhang, D., Simoff, S., Debenham, J.: Informed Recommender: Basing Recommendations on Consumer Product Reviews. IEEE Intelligent Systems 22(3), 39–47 (2007)
Balabanovic, M., Shoham, Y.: Fab: Content-based, Collaborative Recommendation. Communication of the ACM, 66–72 (March 1997)
Linden, G., Smith, B., York, J.: Amazon.com Recommendations Item-to-Item Collaborative Filtering. IEEE Internet Computing, 76–80 (January 2003)
Robertson, S.: Understanding Inverse Document Frequency: on theoretical arguments for IDF. Journal of documentation 60(5), 503–520 (2005)
Salton, G.: Introduction to Modern Information Retrieval. Mcgraw Hill, New York (1983)
Debnath, S., Mitra, P., Pal, N., Giles, C.: Automatic Identification of Informative Sections of Web Pages. IEEE Transactions on Knowledge and Data Engineering 17(9), 1233–1246 (2005)
Chang, S., Sikora, T., Puri, A.: Overview of the MPEG-7 Standard. IEEE Transactions on Circuits and Systems for Video Technology 11(6) (June 2001)
Beil, F., Ester, M., Xu, X.: Frequent Term-Based Text Clustering. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge Discovery and Data Mining, pp. 436–442 (2002)
Pazzani, M., Muramatsu, J., Billsus, D.: Syskill & Webert: Identifying interesting web sites. In: Proceedings of the 13th national conference on Artificial Intelligence (1996)
Chen, L., Sycara, K.: WebMate: A Personal Agent for Browsing and Searching. In: Proceedings of the 2nd international conference on Autonomous Agent, pp. 132–139 (1998)
Pierre, S., Kacan, C., Probst, W.: An agent –based approach for integrating user profile into a knowledge management process. Elsevier Knowledge-Based Systems 13, 307–314 (2000)
Lin, S., Ho, J.: Discovering Informative Content blocks from Web Documents. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge Discovery and Data Mining, July 2002, pp. 588–593 (2002)
Ramaswamy, L., Lyengar, A., Liu, L., Douglis, F.: Automatic Fragment Detection in Dynamic Web Pages and Its Impact on Caching. IEEE Transactions on Knowledge and Data Engineering 17(6), 859–874 (2005)
Armstrong, R., Freitag, D., Joachims, T., Mitchell, T.: WebWatcher: A Learning Apprentice for the World Wide Web (February 1995)
Frakes, W.B., Baeza-Yates, R.: Information Retrieval: Data Structures and Algorithms. Prentice-Hall, Englewood Cliffs (1992)
Yu, Z., Zhou, X.: TV3P: An Adaptive Assistant for Personalized TV. IEEE Transaction on Consumer Electronics 50(1), 393–399 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, S., Jung, M., Lee, E. (2008). A Novel Web Page Analysis Method for Efficient Reasoning of User Preference. In: Lee, S., Choo, H., Ha, S., Shin, I.C. (eds) Computer-Human Interaction. APCHI 2008. Lecture Notes in Computer Science, vol 5068. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70585-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-70585-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70584-0
Online ISBN: 978-3-540-70585-7
eBook Packages: Computer ScienceComputer Science (R0)