Semantically Enriched Web Usage Mining for Personalization
The continuous growth in the size of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills and more sophisticated tools to help the Web user to find the desired information. In order to make Web more user friendly, it is necessary to provide personalized services and recommendations to the Web user. For discovering interesting and frequent navigation patterns from Web server logs many Web usage mining techniques have been applied. The recommendation accuracy of usage based techniques can be improved by integrating Web site content and site structure in the personalization process.
Herein, we propose semantically enriched Web Usage Mining method for Personalization (SWUMP), an extension to solely usage based technique. This approach is a combination of the fields of Web Usage Mining and Semantic Web. In the proposed method, we envisage enriching the undirected graph derived from usage data with rich semantic information extracted from the Web pages and the Web site structure. The experimental results show that the SWUMP generates accurate recommendations and is able to achieve 10-20% better accuracy than the solely usage based model. The SWUMP addresses the new item problem inherent to solely usage based techniques.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1091582Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2641
 Sungjune Park, Nallan Suresh, and Bong-Keun Jeong, "Sequence-based clustering for Web usage mining: A new experimental framework and ANN-enhanced K-means algorithm," Data & Knowledge Engineering, vol. 65, pp. 512–543, 2008.
 Bing Liu, Web Data Mining, Second Edition ed.: Springer, 2011.
 Bamshad Mobasher, Hoghua Dai, Tao Luo, Yuqing Sun, and Jiang Zhu, "Integrating Web Usage and Content Mining for More Effective Personalization," in Proceedings of the International Conference on E-Commerce and Web Technologies, Greenwich, UK, 2000.
 Magdalini Eirinaki, Michalis Vazirgiannis, and Iraklis Varlamis, "Using Site Semantics and a Taxonomy to Enhance the Web Personalization Process," in Proceedings of the 9th ACM International Conference on Knowledge Discovery and Data Mining ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD’03), Washington DC, 2003.
 Mehrdad Jalali, Norwati Mustapha, Md. Nasir Sulaiman, and Ali Mamat, "WebPUM: A Web-based recommendation system to predict user future movements," Expert Systems with Applications, vol. 37, pp. 6201-6212, 2010.
 Tak Woon Yan, Matthew Jacobsen, Hector Garcia-Molina, and Umeshwar Dayal, "From User Access Patterns to Dynamic Hypertext Linking," Computer Networks and ISDN Systems, vol. 28, no. (7–11), pp. 1007–1014, 1996.
 Bamshad Mobasher, Robert Cooley, and Jaideep Srivastava, "Automatic personalization based on Web usage mining," Communications of the ACM, vol. 43, no. 8, pp. 142–151, 2000.
 F. Masseglia, P. Poncelet, and R. Cicchetti, "WebTool: An Integrated Framework for Data Mining," in Proceedings of the 9th International Conference on Database and Expert Systems Applications (DEXA'99), Florence, Italy, 1999, pp. 892-901.
 Bamshad Mobasher, Robert Cooley, and Jaideep Srivastava, "Creating Adaptive Web Sites Through Usage-Based Clustering of URLs," in Proceedings of the 1999 IEEE Knowledge and Data Engineering Exchange Workshop (KDEX'99), November 1999.
 Ranieri Baraglia and Fabrizio Silvestri, "An online recommender system for large Web sites," in Proceedings of the IEEE/WIC/ACM international conference on Web, Beijing, China, 2004.
 Dimitrios Pierrakos, Georgios Paliouras, Christos Papatheodorou, and Constantine D. Spyropoulos, "KOINOTITES:A Web Usage Mining Tool for Personalization," in Proceedings of the Panhellenic Conference on Human Computer Interaction, 2001.
 B. Zhou, S. C. Hui, and K. Chang, "An intelligent recommender system using sequential Web access patterns," in IEEE conference on cybernetics and intelligent systems, 2004, pp. 393–398.
 José Borges and Mark Levene, "Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions," IEEE Trans. on Knowledge And Data Engineering, vol. 19, no. 4, pp. 441 – 452, Apr 2007.
 Magdalini Eirinaki, Dimitrios Mavroeidis, George Tsatsaronis, and Michalis Vazirgiannis, "Introducing Semantics in Web Personalization: The Role of Ontologies," in Proc. EWMF/KDO'2005, 2005, pp. 147-162.
 Stuart Middleton, Nigel Shadbolt, and David Roure, "Ontological User Profiling in Recommender Systems," ACM Transactions on Information Systems, vol. 22, no. 1, pp. 54–88, 2004.
 Haibin Liu and Vlado Kešelj, "Combined mining of Web server logs and Web contents for classifying user navigation patterns and predicting users’ future requests," Data & Knowledge Engineering, vol. 61, no. 2, pp. 304–330, 2007.
 Xin Jin, Yanzan Zhou, and Bamshad Mobasher, "A Unified Approach to Personalization Based on Probabilistic Latent Semantic Models of Web Usage and Content," in AAAI Workshop on Semantic Web Personalization (SWP’04), July 2004.
 Miao Wan, Arne Jönsson, Cong Wang, and Lixiang Li, "Web user clustering and Web prefetching using Random Indexing with weight functions," Knowl Information Systems, October 2011.
 Pinar Senkul and Suleyman Salin, "Improving pattern quality in web usage mining by using semantic information," Knowledge and Information Systems, p. 2011.
 Thi Thanh Sang Nguyen, Hai Yan Lu, and Jie Lu, "Ontology-Style Web Usage Model for Semantic Web Applications," in 10th Int’l Conference on Intelligent Systems Design and Applications (ISDA), 2010, pp. 784-789.
 Juan D. Velásquez, Luis E. Dujovne, and Gaston L’Huillier, "Extracting significant Website Key Objects: A Semantic Web mining approach mining approach ," Engineering Applications of Artificial Intelligence, vol. 24, pp. 1532-1541, March 2011.
 Mehdi Adda, Petko Valtchev, and Rokia Missaoui, "A framework for mining meaningful usage patterns within a semantically enhanced web portal," in Proceedings of the Third C* Conference on Computer Science and Software Engineering C3S2E '10, New York, USA, 2010, pp. 138-147.
 Julia Hoxha, Martin Junghans, and Sudhir Agarwal, "Enabling Semantic Analysis of User Browsing Patterns in the Web of Data," in Julia Hoxha, Martin Junghans, Sudhir Agarwal, Lyon, France, 2012.
 R. Cooley, B. Mobasher, and J. Srivastava, "Data preparation for mining world wide web browsing patterns," Knowledge and Information System, vol. 1, pp. 5–32, 1999.
 Gunnar Grimnes, Peter Edwards, and Alun Preece, "Instance Based Clustering of Semantic Web Resources," in Proceedings of the 5th European Semantic Web Conference, LNCS Springer-Verlag, 2008.
 N. R Mabroukeh and C. I. Ezeife, "Semantic-rich Markov Models for Web Prefetching," in IEEE International Conference on Data Mining Workshops, 2009, pp. 465-470.
 G. Castellano, A. M. Fanelli, and M. A. Torsello, "NEWER: A system for NEuro-fuzzy WEb Recommendation," Applied Soft Computing, vol. 11, no. 1, pp. 793-806, January 2011.
 Sergey Brin and Lawrence Page, "The Anatomy of a Large-Scale Hypertextual Web Search Engine," Computer Networks, vol. 30, no. 1-7, pp. 107-117, 1998.
 Alberto Apostolico, "String editing and longest common subsequences," in Handbook of Formal Languages., 1997, pp. 361–398.
 Andrija Tomovic, Predrag Janicic, and Vlado Kešelj, "N-gram-based classification and hierarchical clustering of genome sequences," Computer Methods and Programs in Biomedicine, 2005.
 Peter I. Hofgesang and Jan Peter Patist, "On Modelling and Synthetically Generating Web Usage Data," in Int’l Conference on Web Intelligence and Intelligent Agent Technology, 2008, pp. 98-102.
 R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval.: Addison Wesley, 1999.