Abstract
In this paper, we present a class of rules, called context-topic rules, for discovering associations between topics and contexts, where a context is defined as a set of features that can be extracted from the log file of a Web search engine. We introduce a notion of rule interestingness that measures the level of the interest of the topic within a context, and provide an algorithm to compute concise representations of interesting context-topic rules. Finally, we present the results of applying the methodology proposed to a large data log of a search engine.
Carlos A. Hurtado was supported by Millennium Nucleus, Center for Web Research (P04-067-F), Mideplan, Chile.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data. Data Min. Knowl. Discov. 11(1), 5–33 (2005)
Borges, J., Levene, M.: Data mining of user navigation patterns. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, pp. 92–112. Springer, Heidelberg (2000)
Eppstein, D.: Arboricity and bipartite subgraph listing algorithms. Information Processing Letters 51, 207–211 (1994)
Fleiss, J.L., Levin, B., Paik, M.C.: Statistical Methods for Rates and Proportions. Wiley, Chichester (2003)
Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining – A general survey and comparison. SIGKDD Explorations 2, 58–64 (2000)
Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press, Cambridge (2001)
Li, Y., Ning, P., Wang, X.S., Jajodia, S.: Discovering calendar-based temporal association rules. Data Knowl. Eng. 44(2), 193–218 (2003)
Pu, K., Mendelzon, A.: Concise descriptions of subsets of structured sets. ACM Trans. Database Syst. 30 (1) (2005)
Zaki, M.J., Ogihara, M.: Theoretical foundations of association rules. In: Proceedings of 3rd SIGMOD 1998 Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD 1998), Seattle, Washington (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hurtado, C.A., Levene, M. (2006). Discovering Context-Topic Rules in Search Engine Logs. In: Crestani, F., Ferragina, P., Sanderson, M. (eds) String Processing and Information Retrieval. SPIRE 2006. Lecture Notes in Computer Science, vol 4209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880561_29
Download citation
DOI: https://doi.org/10.1007/11880561_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45774-9
Online ISBN: 978-3-540-45775-6
eBook Packages: Computer ScienceComputer Science (R0)