Skip to main content
SpringerLink
Log in

Web Usage Mining

  • Chapter
  • First Online:
Web Data Mining

Part of the book series: Data-Centric Systems and Applications ((DCSA))

Abstract

With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, the volumes of clickstream, transaction data, and user profile data collected by Web-based organizations in their daily operations has reached astronomical proportions. Analyzing such data can help these organizations determine the life-time value of clients, design cross-marketing strategies across products and services, evaluate the effectiveness of promotional campaigns, optimize the functionality of Web-based applications, provide more personalized content to visitors, and find the most effective logical structure for their Web space. This type of analysis involves the automatic discovery of meaningful patterns and relationships from a large collection of primarily semi-structured data, often stored in Web and applications server access logs, as well as in related operational data sources.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 89.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. Adomavicius, G. and A. Tuzhilin. Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): p. 734–749.

    Article  Google Scholar 

  2. Agarwal, D. Statistical Challenges in Online Advertising. In Tutorial given at ACM KDD-2009 conference, 2009.

    Google Scholar 

  3. Agarwal, D. and B.-C. Chen. fLDA: matrix factorization through latent dirichlet allocation. In Proceedings of the third ACM international conference on Web search and data mining. 2010, ACM: New York, New York, USA. p. 91–100.

    Google Scholar 

  4. Agarwal, R., C. Aggarwal, and V. Prasad. A tree projection algorithm for generation of frequent item sets. Journal of Parallel and Distributed Computing, 2001, 61(3): p. 350–371.

    Article  MATH  Google Scholar 

  5. Anand, S. and B. Mobasher. Intelligent techniques for web personalization. Intelligent Techniques for Web Personalization, 2005: p. 1–36.

    Google Scholar 

  6. Baeza-Yates, R. Applications of web query mining. Advances in Information Retrieval, 2005: p. 7–22.

    Google Scholar 

  7. Baeza-Yates, R. Graphs from search engine queries. SOFSEM 2007: Theory and Practice of Computer Science, 2007: p. 1–8.

    Google Scholar 

  8. Baeza-Yates, R., C. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. In Proceedings of International Workshop on Clustering Information over the Web, 2004.

    Google Scholar 

  9. Baeza-Yates, R. and F. Saint-Jean. A three level search engine index based in query log distribution. In Proceedings of SPIRE 2003, 2003.

    Google Scholar 

  10. Baeza-Yates, R. and A. Tiberi. Extracting semantic relations from query logs. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2007), 2007.

    Google Scholar 

  11. Balabanovi, M. and Y. Shoham. Fab: content-based, collaborative recommendation. Communications of the ACM, 1997, 40(3): p. 66–72.

    Article  Google Scholar 

  12. Beeferman, D. and A. Berger. Agglomerative clustering of a search engine query log. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-, 2000.

    Google Scholar 

  13. Berendt, B., B. Mobasher, M. Nakagawa, and M. Spiliopoulou. The impact of site structure and user environment on session reconstruction in web usage analysis. WEBKDD 2002, 2003: p. 159–179.

    Google Scholar 

  14. Berendt, B. and M. Spiliopoulou. Analysis of navigation behaviour in web sites integrating multiple information systems. The VLDB Journal, 2000, 9(1): p. 56–75.

    Article  Google Scholar 

  15. Borges, J. and M. Levene. Data mining of user navigation patterns. Web usage analysis and user profiling, 2000: p. 92–112.

    Google Scholar 

  16. Breese, J.S., D. Heckerman, and C. Kadie. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998.

    Google Scholar 

  17. Brusilovsky, P., A. Kobsa, and W. Nejdl. Adaptive Web: Methods and Strategies of Web Personalization. 2007, Berlin: Springer.

    Google Scholar 

  18. Büchner, A. and M. Mulvenna. Discovering internet marketing intelligence through online analytical web usage mining. ACM SIGMOD Record, 1998, 27(4): p. 54–61.

    Article  Google Scholar 

  19. Cadez, I., D. Heckerman, C. Meek, P. Smyth, and S. White. Model-based clustering and visualization of navigation patterns on a web site. Data Mining and Knowledge Discovery, 2003, 7(4): p. 399–424.

    Article  MathSciNet  Google Scholar 

  20. Cao, H., D. Jiang, J. Pei, E. Chen, and H. Li. Towards context-aware search by learning a very large variable length hidden markov model from search logs. In Proceedings of International Conference on World Wide Web

    Google Scholar 

  21. (WWW-2009), 2009. 21. Cao, H., D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware

    Google Scholar 

  22. query suggestion by mining click-through and session data. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008), 2008.

    Google Scholar 

  23. Castillo, C., C. Corsi, D. Donato, P. Ferragina, and A. Gionis. Query-log mining for detecting polysemy and spam. In Proceedings of the WebKDD 2008: 10 years of Knowledge Discovery on the Web, 2008.

    Google Scholar 

  24. Castillo, C., D. Donato, A. Gionis, V. Murdock, and F. Silvestri. Know your neighbors: Web spam detection using the web topology. In Proceedings of ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR-2007), 2007: ACM.

    Google Scholar 

  25. Catledge, L. and J. Pitkow. Characterizing browsing strategies in the World- Wide Web. Computer Networks and ISDN Systems, 1995, 27(6): p. 1065–1073.

    Article  Google Scholar 

  26. Chien, S. and N. Immorlica. Semantic similarity between search engine queries using temporal correlation. In Proceedings of International Conference on World Wide Web (WWW-2005), 2005.

    Google Scholar 

  27. Chuang, S. and L. Chien. Enriching web taxonomies through subject categorization of query terms from search engine logs. Decision Support Systems, 2003, 35(1): p. 113–127.

    Article  Google Scholar 

  28. Cooley, R., B. Mobasher, and J. Srivastava. Data preparation for mining world wide web browsing patterns. Knowledge and Information systems, 1999, 1(1): p. 5–32.

    Google Scholar 

  29. Cooley, R., B. Mobasher, and J. Srivastava. Web mining: Information and pattern discovery on the world wide web. In Proceedings of Intl. Conf. on Tools With Artificial Intelligence (ICTAI-1997), 1997.

    Google Scholar 

  30. Craswell, N. and M. Szummer. Random walks on the click graph. In Proceedings of ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR-2007), 2007.

    Google Scholar 

  31. Cui, H., J. Wen, J. Nie, and W. Ma. Probabilistic query expansion using query logs. In Proceedings of International Conference on World Wide Web (WWW-2002), 2002.

    Google Scholar 

  32. Cui, H., J. Wen, J. Nie, and W. Ma. Query expansion by mining user logs. IEEE Transactions on Knowledge and Data Engineering, 2003: p. 829–839.

    Google Scholar 

  33. Dai, W., Y. Yu, C. Zhang, J. Han, and G. Xue. A Novel Web Page Categorization Algorithm Based on Block Propagation Using Query-Log Information. Advances in Web-Age Information Management, 2006: p. 435–446.

    Google Scholar 

  34. David, M.P., H. Eric, L. Steve, and C.L. Giles. Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence. 2000, Morgan Kaufmann Publishers Inc.

    Google Scholar 

  35. Davison, B., D. Deschenes, and D. Lewanda. Finding relevant website queries. In Proceedings of International Conference on World Wide Web (WWW-2003), 2003.

    Google Scholar 

  36. Dempster, A., N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1977, 39(1): p. 1–38.

    Google Scholar 

  37. Deshpande, M. and G. Karypis. Selective Markov models for predicting Web page accesses. ACM Transactions on Internet Technology (TOIT), 2004, 4(2): p. 163–184.

    Article  Google Scholar 

  38. Edelman, B., M. Ostrovsky, and M. Schwarz. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. The American Economic Review, 2007, 97(1): p. 242–259.

    Article  Google Scholar 

  39. Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth. eds. From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining. 1996, AAAI/MIT Press. 1–34.

    Google Scholar 

  40. Felfernig, A., G. Friedrich, and L. Schmidt-Thieme. Guest Editors' Introduction: Recommender Systems. IEEE Intelligent Systems, 2007, 22(3): p. 18–21.

    Article  Google Scholar 

  41. Fetterly, D. Adversarial information retrieval: The manipulation of web content. ACM Computing Reviews, 2007.

    Google Scholar 

  42. Flake, G., E. Glover, S. Lawrence, and C. Giles. Extracting query modifications from nonlinear SVMs. In Proceedings of International Conference on World Wide Web (WWW-2002), 2002.

    Google Scholar 

  43. Fonseca, B., P. Golgher, E. De Moura, and N. Ziviani. Using association rules to discover search engines related queries. In LA-WEB-2003, 2003.

    Google Scholar 

  44. Fu, X., J. Budzik, and K. Hammond. Mining navigation history for recommendation. In Proceedings of Intl. Conf. on Intelligent User Interfaces, 2000.

    Google Scholar 

  45. Funk, S. Netflix Update: Try This At Home. 2006; Available from: http://sifter.org/~simon/journal/20061211.html.

  46. Goldberg, K.E.N. Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval, 2001.

    Google Scholar 

  47. Gyöngyi, Z. and H. Garcia-Molina. Link spam alliances. In Proceedings of International Conference on Very Large Data Bases (VLDB-2005), 2005: VLDB Endowment.

    Google Scholar 

  48. Hansen, M. and E. Shriver. Using navigation data to improve IR functions in the context of web search. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2001), 2001.

    Google Scholar 

  49. Herlocker, J., J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 2004, 22(1): p. 5–53.

    Article  Google Scholar 

  50. Hill, W., L. Stead, M. Rosenstein, and G. Furnas. Recommending and evaluating choices in a virtual community of use. In ACM Conference on Human Factors in Computing Systems, CHI'95, 1995.

    Google Scholar 

  51. Hillard, D., S. Schroedl, E. Manavoglu, H. Raghavan, and C. Leggetter. Improving ad relevance in sponsored search. In Proceedings of ACM International Conference on Web Search and Data Mining (WSDM-2010), 2010.

    Google Scholar 

  52. Hofmann, T. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 2004, 22(1): p. 89–115.

    Article  Google Scholar 

  53. Hofmann, T. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 2001, 42(1): p. 177–196.

    Article  MATH  Google Scholar 

  54. Huang, C., L. Chien, and Y. Oyang. Relevant term suggestion in interactive web search based on contextual information in query session logs. Journal of the American Society for Information Science and Technology, 2003, 54(7): p. 638–649.

    Article  Google Scholar 

  55. Huang, Z., H. Chen, and D. Zeng. Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering. ACM Transactions on Information Systems (TOIS), 2004, 22(1): p. 116–142.

    Article  Google Scholar 

  56. Huang, Z., D.D. Zeng, and H. Chen. Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems. Management Science, 2007, 53(7): p. 1146–1164.

    Article  Google Scholar 

  57. Jansen, B., A. Spink, and V. Kathuria. How to define searching sessions on web search engines. Advances in Web Mining and Web Usage Analysis, 2007: p. 92–109.

    Google Scholar 

  58. Jasson, D.M.R. and S. Nathan. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of International Conference on Machine Learning (ICML-2005). 2005.

    Google Scholar 

  59. Jin, R., L. Si, and C. Zhai. A Study of Mixture Models for Collaborative Filtering. Information Retrieval, 2006, 9(3): p. 357–382.

    Article  Google Scholar 

  60. Jin, X., Y. Zhou, and B. Mobasher. Web usage mining based on probabilistic latent semantic analysis. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), 2004.

    Google Scholar 

  61. Joachims, T. Optimizing search engines using clickthrough data. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2002), 2002.

    Google Scholar 

  62. Jones, R., B. Rey, O. Madani, and W. Greiner. Generating query substitutions. In Proceedings of International Conference on World Wide Web (WWW-2006), 2006.

    Google Scholar 

  63. Kang, H., K. Wang, D. Soukal, F. Behr, and Z. Zheng. Large-scale bot detection for search engines. In Proceedings of International Conference on World Wide Web (WWW-2010), 2010.

    Google Scholar 

  64. Kimball, R., R. Merz, and I. Books24x7. The data webhouse toolkit: building the web-enabled data warehouse. 2000: Wiley New York.

    Google Scholar 

  65. Kohavi, R., L. Mason, R. Parekh, and Z. Zheng. Lessons and challenges from mining retail e-commerce data. Machine Learning, 2004, 57(1): p. 83–113.

    Article  Google Scholar 

  66. Kolda, T.G. and B.W. Bader. Tensor Decompositions and Applications. SIAM Rev., 2009, 51(3): p. 455–500.

    Article  MATH  MathSciNet  Google Scholar 

  67. Konstan, J.A. Introduction to Recommender Systems: Algorithms and Evaluation. ACM Transactions on Information Systems, 2004, 22(1): p. 1–4.

    Article  Google Scholar 

  68. Koren, Y. The BellKor Solution to the Netflix Grand Prize. In http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf , 2009.

  69. Koren, Y. Collaborative filtering with temporal dynamics. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009), 2009.

    Google Scholar 

  70. Koren, Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008), 2008.

    Google Scholar 

  71. Koren, Y., R. Bell, and C. Volinsky. Matrix Facorization Techniques for Recommender Systems. IEEE Computer, 2009, 42(8): p. 30–37.

    Google Scholar 

  72. Kraft, R. and J. Zien. Mining anchor text for query refinement. In Proceedings of International Conference on World Wide Web (WWW-2004), 2004.

    Google Scholar 

  73. Kumar, R. and A. Tomkins. A characterization of online search behavior. IEEE Data Eng. Bull., 2009, 32(2): p. 3–11.

    MATH  Google Scholar 

  74. Lathia, N., S. Hailes, L. Capra, and X. Amatriain. Temporal diversity in recommender systems. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 2010, ACM: Geneva, Switzerland. p. 210–217.

    Google Scholar 

  75. Lau, T. and E. Horvitz. Patterns of search: analyzing and modeling Web query refinement. In Proceedings of the Seventh International Conference on User Modeling, 1999.

    Google Scholar 

  76. Lee, D.D. and H.S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401(6755): p. 788–791.

    Article  Google Scholar 

  77. Lin, W., S. Alvarez, and C. Ruiz. Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 2002, 6(1): p. 83–105.

    Article  MathSciNet  Google Scholar 

  78. Linden, G., B. Smith, and J. York. Amazon.com recommendations: item-toitem collaborative filtering. IEEE Internet Computing, 2003, 7(1): p. 76–80.

    Google Scholar 

  79. Liu, B., W. Hsu, and Y. Ma. Mining association rules with multiple minimum supports. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-1999), 1999.

    Google Scholar 

  80. Liu, T. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 2009, 3(3): p. 225–331.

    Article  Google Scholar 

  81. Liu, Y., R. Cen, M. Zhang, S. Ma, and L. Ru. Identifying web spam with user behavior analysis. In Proceedings of 5th Workshop on Adversarial Information Retrieval on the Web (AIRWeb), 2008.

    Google Scholar 

  82. Ma, H., H. Yang, I. King, and M. Lyu. Learning latent semantic relations from clickthrough data for query suggestion. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2008), 2008.

    Google Scholar 

  83. Mobasher, B. Web usage mining. John Wang (eds.), Encyclopedia of Data Warehousing and Mining, Idea Group, 2006: p. 449–483.

    Google Scholar 

  84. Mobasher, B. Web Usage Mining and Personalization. Munindar P. Singh (ed.), Practical Handbook of Internet Computing. CRC Press, 2005.

    Google Scholar 

  85. Mobasher, B., R. Cooley, and J. Srivastava. Automatic personalization based on Web usage mining. Communications of the ACM, 2000, 43(8): p. 142–151.

    Article  Google Scholar 

  86. Mobasher, B., H. Dai, T. Luo, and M. Nakagawa. Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery, 2002, 6(1): p. 61–82.

    Article  MathSciNet  Google Scholar 

  87. Mobasher, B., H. Dai, T. Luo, and M. Nakagawa. Effective personalization based on association rule discovery from web usage data. In Proceedings of ACM Workshop on Web Information and Data Management, 2001.

    Google Scholar 

  88. Nasraoui, O., R. Krishnapuram, and A. Joshi. Mining web access logs usinga fuzzy relational clustering algorithm based on a robust estimator. In Proceedings of International Conference on World Wide Web (WWW-1999), 1999.

    Google Scholar 

  89. Nasraoui, O., M. Soliman, E. Saka, A. Badia, and R. Germain. A web usage mining framework for mining evolving user profiles in dynamic web sites. IEEE Transactions on Knowledge and Data Engineering, 2007: p. 202–215.

    Google Scholar 

  90. Ntoulas, A., M. Najork, M. Manasse, and D. Fetterly. Detecting spam web pages through content analysis. In Proceedings of International Conference on World Wide Web (WWW-2006), 2006.

    Google Scholar 

  91. Ohura, Y., K. Takahashi, I. Pramudiono, and M. Kitsuregawa. Experiments on query expansion for internet yellow page services using web log mining. In Proceedings of International Conference on Very Large Data Bases (VLDB-2002), 2002.

    Google Scholar 

  92. Paliouras, G., C. Papatheodorou, V. Karkaletsis, and C. Spyropoulos. Discovering user communities on the Internet using unsupervised machine learning techniques. Interacting with Computers, 2002, 14(6): p. 761–791.

    Article  Google Scholar 

  93. Paterek, A. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD Cup and Workshop 2007, 2007.

    Google Scholar 

  94. Paul, R., I. Neophytos, S. Mitesh, B. Peter, and R. John. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work. 1994.

    Google Scholar 

  95. Peng, J. and D. Zeng. Exploring Information Hidden in Tags: A Subjectbased Item Recommendation Approach. In Proceedings of 19th Workshop on Information Technologies and Systems, 2009. Phoenix, USA.

    Google Scholar 

  96. Peng, J., D. Zeng, B. Liu, and H. Zhao. CFUI: Collaborative Filtering with Unlabeled Items. In Proceedings of 20th Workshop on Information Technologies and Systems, 2010. St. Louis, Missouri, USA.

    Google Scholar 

  97. Peng, J., D. Zeng, H. Zhao, and F.-Y. Wang. Collaborative Filtering in Social Tagging Systems Based on Joint Item-Tag Recommendations. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010, ACM: Toronto, Canada.

    Google Scholar 

  98. Pierrakos, D., G. Paliouras, C. Papatheodorou, and C. Spyropoulos. Web usage mining as a tool for personalization: A survey. User Modeling and User-Adapted Interaction, 2003, 13(4): p. 311–372.

    Article  Google Scholar 

  99. Pitkow, J. and P. Pirolli. Mining longest repeating subsequences to predict world wide web surfing. In Proceedings of USENIX Symposium on Internet Technologies and Systems, 1999: USENIX Association.

    Google Scholar 

  100. Puppin, D. and F. Silvestri. The query-vector document model. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2006), 2006.

    Google Scholar 

  101. Resnick, P., N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In ACM Conference on Computer-Supported Cooperative Work, 1994.

    Google Scholar 

  102. Richardson, M., E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of International Conference on World Wide Web (WWW-2007), 2007: ACM.

    Google Scholar 

  103. Salakhutdinov, R. and A. Mnih. Probabilistic Matrix Factorization. Advances in Neural Information Processing Systems, 2008, 20: p. 1257–1264.

    Google Scholar 

  104. Saraiva, P., E. Silva de Moura, N. Ziviani, W. Meira, R. Fonseca, and B. Riberio-Neto. Rank-preserving two-level caching for scalable search engines. In Proceedings of ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR-2001), 2001. 104. Sarukkai, R. Link prediction and path analysis using Markov chains1. Computer Networks, 2000, 33(1–6): p. 377–386.

    Google Scholar 

  105. Sarwar, B., G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proceedings of International Conference on World Wide Web (WWW-2001), 2001.

    Google Scholar 

  106. Sarwar, B., G. Karypis, J. Konstan, and J. Riedl. Application of Dimensionality Reduction in Recommender Systems: a case study. In Proceedings of WebKDD Workshop at the ACM SIGKKD, 2000.

    Google Scholar 

  107. Shardanand, U. and P. Maes. Social Information Filtering: Algorithms for Automating Word of Mouth. In ACM Conference on Human Factors in Computing Systems, 1995. Denver, CO.

    Google Scholar 

  108. Shen, D., J. Sun, Q. Yang, and Z. Chen. Building bridges for web query classification. In Proceedings of ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR-2006), 2006.

    Google Scholar 

  109. Shen, D., J. Sun, Q. Yang, and Z. Chen. A comparison of implicit and explicit links for web page classification. In Proceedings of International Conference on World Wide Web (WWW-2006), 2006.

    Google Scholar 

  110. Si, L. and R. Jin. Flexible mixture model for collaborative filtering. In Proceedings of the 20th International Conference on Machine Learning. 2003.

    Google Scholar 

  111. Silverstein, C., H. Marais, M. Henzinger, and M. Moricz. Analysis of a very large web search engine query log. SIGIR Forum, 1999.

    Google Scholar 

  112. Song, R., Z. Luo, J. Wen, Y. Yu, and H. Hon. Identifying ambiguous queries in web search. In Proceedings of International Conference on World Wide Web (WWW-2007), 2007.

    Google Scholar 

  113. Spiliopoulou, M. Web usage mining for web site evaluation. Communications of the ACM, 2000, 43(8): p. 127–134.

    Article  Google Scholar 

  114. Spiliopoulou, M. and L. Faulstich. WUM: a tool for web utilization analysis. The World Wide Web and Databases, 1999: p. 184–203.

    Google Scholar 

  115. Spiliopoulou, M., B. Mobasher, B. Berendt, and M. Nakagawa. A framework for the evaluation of session reconstruction heuristics in web-usage analysis. INFORMS Journal on Computing, 2003, 15(2): p. 171–190.

    Article  Google Scholar 

  116. Spink, A., B. Jansen, D. Wolfram, and T. Saracevic. From e-sex to ecommerce: Web search changes. Computer, 2002, 35(3): p. 107–109.

    Article  Google Scholar 

  117. Spink, A., D. Wolfram, M. Jansen, and T. Saracevic. Searching the web: The public and their queries. Journal of the American Society for Information Science and Technology, 2001, 52(3): p. 226–234.

    Article  Google Scholar 

  118. Srivastava, J., R. Cooley, M. Deshpande, and P. Tan. Web usage mining: Discovery and applications of usage patterns from web data. ACM SIGKDD Explorations Newsletter, 2000, 1(2): p. 12–23.

    Article  Google Scholar 

  119. Takacs, G., I. Pilaszy, G. Nemeth, and D. Tikk. On the Gravity Recommendation System. In Processings of KDD Cup and Workshop 2007, 2007.

    Google Scholar 

  120. Tan, P. and V. Kumar. Discovery of web robot sessions based on their navigational patterns. Data Mining and Knowledge Discovery, 2002, 6(1): p. 9–35.

    Article  MathSciNet  Google Scholar 

  121. Tanasa, D. and B. Trousse. Advanced data preprocessing for intersites web usage mining. Intelligent Systems, IEEE, 2005, 19(2): p. 59–65.

    Article  Google Scholar 

  122. Tso-Sutter, K.H.L., L.B. Marinho, and L. Schmidt-Thieme. Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms. In Proceedings of the ACM symposium on Applied computing. 2008, ACM: Fortaleza, Ceara, Brazil. p. 1995–1999.

    Google Scholar 

  123. Wagman, A. Netflix SVD Derivation. 2007; Available from: http://sifter.org/~simon/journal/20070815.html.

  124. Wen, J., J. Nie, and H. Zhang. Clustering user queries of a search engine. In Proceedings of International Conference on World Wide Web (WWW-2001), 2001.

    Google Scholar 

  125. Wetzker, R., W. Umbrath, and A. Said. A Hybrid Approach to Item Recommendation in Folksonomies. In Proceedings of the WSDM'09 Workshop on Exploiting Semantic Annotations in Information Retrieval. 2009, ACM: Barcelona, Spain. p. 25–29.

    Google Scholar 

  126. Wu, B. and B. Davison. Detecting semantic cloaking on the web. In Proceedings of International Conference on World Wide Web (WWW-2006), 2006.

    Google Scholar 

  127. Xiang, L., Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. Temporal recommendation on graphs via long- and short-term preference fusion. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 2010, ACM: Washington, DC, USA. p. 723–732.

    Google Scholar 

  128. Ypma, A. and T. Heskes. Automatic categorization of web pages and user clustering with mixtures of hidden Markov models. In Proceedings of Mining Web Data for Discovering Usage Patterns and Profiles (WEBKDD-2002), 2003.

    Google Scholar 

  129. Yu, K., J. Lafferty, S. Zhu, and Y. Gong. Large-scale collaborative prediction using a nonparametric random effects model. In Proceedings of the 26th Annual International Conference on Machine Learning. 2009, ACM: Montreal, Quebec, Canada. p. 1185–1192.

    Google Scholar 

  130. Zhang, Z. and O. Nasraoui. Mining search engine query logs for query recommendation. In Proceedings of International Conference on World Wide Web (WWW-2006), 2006.

    Google Scholar 

  131. Zhang, Z. and O. Nasraoui. Mining search engine query logs for social filtering-based query recommendation. Applied Soft Computing, 2008, 8(4): p. 1326–1334.

    Article  Google Scholar 

  132. Zhen, Y., W. Li, and D. Yeung. TagiCoFi: Tag Informed Collaborative Filtering. In Proceedings of the 3rd ACM conference on Recommender systems. 2009. p. 69–76.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Illinois, Chicago, 851 S. Morgan St., Chicago, IL, 60607-7053, USA

    Bing Liu

Authors
  1. Bing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bamshad Mobasher
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olfa Nasraoui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bing Liu .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Liu, B., Mobasher, B., Nasraoui, O. (2011). Web Usage Mining. In: Web Data Mining. Data-Centric Systems and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19460-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19460-3_12

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19459-7

  • Online ISBN: 978-3-642-19460-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation