Variable Length Markov Chains for Web Usage Mining

Variable Length Markov Chains for Web Usage Mining

José Borges (School of Engineering, University of Porto, Portugal) and Mark Levene (Birkbeck, University of London, UK)
Copyright: © 2009 |Pages: 5
ISBN13: 9781605660103|ISBN10: 1605660108|EISBN13: 9781605660110
DOI: 10.4018/978-1-60566-010-3.ch310
Cite Chapter Cite Chapter

MLA

Borges, José and Mark Levene. "Variable Length Markov Chains for Web Usage Mining." Encyclopedia of Data Warehousing and Mining, Second Edition. IGI Global, 2009. 2031-2035. Web. 11 Jan. 2020. doi:10.4018/978-1-60566-010-3.ch310

APA

Borges, J., & Levene, M. (2009). Variable Length Markov Chains for Web Usage Mining. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining, Second Edition (pp. 2031-2035). Hershey, PA: IGI Global. doi:10.4018/978-1-60566-010-3.ch310

Chicago

Borges, José and Mark Levene. "Variable Length Markov Chains for Web Usage Mining." In Encyclopedia of Data Warehousing and Mining, Second Edition, ed. John Wang, 2031-2035 (2009), accessed January 11, 2020. doi:10.4018/978-1-60566-010-3.ch310

Export Reference

Mendeley
Favorite

Abstract

Web usage mining is usually defined as the discipline that concentrates on developing techniques that model and study users’ Web navigation behavior by means of analyzing data obtained from user interactions with Web resources; see (Mobasher, 2006; Liu, 2007) for recent reviews on web usage mining. When users access Web resources they leave a trace behind that is stored in log files, such traces are called clickstream records. Clickstream records can be preprocessed into time-ordered sessions of sequential clicks (Spiliopoulou et al., 2003), where a user session represents a trail the user followed through the Web space. The process of session reconstruction is called sessionizing. Understanding user Web navigation behavior is a fundamental step in providing guidelines on how to improve users’ Web experience. In this context, a model able to represent usage data can be used to induce frequent navigation patterns, to predict future user navigation intentions, and to provide a platform for adapting Web pages according to user specific information needs (Anand et al., 2005; Eirinaki et al., 2007). Techniques using association rules (Herlocker et al., 2004) or clustering methods (Mobasher et al., 2002) have been used in this context. Given a set of transactions clustering techniques can be used, for example, to find user segments, and association rule techniques can be used, for example, to find important relationships among pages based on the users navigational patterns. These methods have the limitation that the ordering of page views is not taken into consideration in the modeling of user sessions (Liu, 2007). Two methods that take into account the page view ordering are: tree based methods (Chen et al., 2003) used for prefetching Web resources, and Markov models (Borges et al., 2000; Deshpande et al., 2004) used for link prediction. Moreover, recent studies have been conducted on the use of visualization techniques for discovering navigational trends from usage data (Chen et al., 2007a; Chen et al., 2007b).

References

Anand S. Mobasher B. (2005). Intelligent Techniques for Web Personalization.Lecture Notes in Computer Science, LNAI, 3169, 1–36. 10.1007/11577935_1
Ankolekar, A., Krotzsch, M., Tran, T., & Vrandecic, D. (2007). The two cultures: mashing up web 2.0 and the semantic web. Proceedings of the 16th international conference on World Wide Web, 825-834.
Bejerano G. (2004). Algorithms for variable length Markov chain modeling.Bioinformatics (Oxford, England), 20(5), 788–789. 10.1093/bioinformatics/btg48914751999
Borges, J., & Levene, M. (2000). Data mining of user navigation patterns. In Web Usage Analysis and User Profiling, LNAI 1836, 92-111.
Borges J. Levene M. (2005). Generating dynamic higher-order Markov models in Web usage mining.LNAI, 3721, 34–45.
Borges J. Levene M. (2007a). Testing the predictive power of variable history Web usage.Soft Computing, 11(8), 717–727. 10.1007/s00500-006-0115-1
Borges J. Levene M. (2007b). Evaluating variable length Markov chain models for analysis of user Web navigation sessions.IEEE Transactions on Knowledge and Data Engineering, 19(4), 441–452. 10.1109/TKDE.2007.1012
Chen, J., Zheng, T., Thorne, W., Huntley, D., Zayane, O. R., & Goebel, R. (2007a). Visualizing Web Navigation Data with Polygon Graphs. Proceedings of the 11th International Conference Information Visualization, 232-237.
Chen, J., Zheng, T., Thorne, W., Zayane, O. R., & Goebel, R. (2007b). Visual Data Mining of Web Navigational Data. Proceedings of the 11th International Conference Information Visualization, 649-656.
Chen X. Zhang X. (2003). A popularity-based prediction model for Web prefetching.IEEE Computer, 36(3), 63–70.
Deshpande M. Karypis G. , G. (2004). Selective Markov models for predicting Web page accesses.ACM Transactions on Internet Technology, 4(2), 163–184. 10.1145/990301.990304
Dongshan X. Juni S. (2002). A new Markov model for Web access prediction.IEEE Computational Science and Engineering, 4(6), 34–39.
Eirinaki M. Vazirgiannis M. (2007). Web site personalization based on link analysis and navigational patterns.ACM Transactions on Internet Technology, 7(4). 10.1145/1278366.1278370
Fagin R. Kumar R. Sivakumar D. (2003). Comparing Top k Lists. SIAM Journal on Discrete Mathematics, 17(1), 134–160. 10.1137/S0895480102412856
Herlocker J. L. Konstan J. A. Terveen L. G. Riedl J. (2004). Evaluating collaborative filtering recommender systems.ACM Transactions on Information Systems, 22(1), 5–53. 10.1145/963770.963772
Koychev I. (2007). Experiments with two approaches for tracking drifting concepts.Serdica Journal of Computing, 1(1), 27–44.
Liu, B. (2007). Web Data Mining - Exploring Hyperlinks, Contents, and Usage Data, Springer.
Mobasher, B. (2006). Web Usage Mining, In Encyclopedia of Data Warehousing and Mining, Idea Group Reference, 1216-1220.
Mobasher B. Dai H. Luo T. Nakagawa M. (2002). Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization.Data Mining and Knowledge Discovery, 6(1), 61–82. 10.1023/A:1013232803866
Spiliopoulou M. Mobasher B. Berendt B. Nakagawa M. (2003). A framework for the evaluation of session reconstruction heuristics in Web usage analysis.INFORMS Journal on Computing, 15(2), 171–190. 10.1287/ijoc.15.2.171.14445

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.