LinkSelector: A Web mining approach to hyperlink selection for Web portals

ABSTRACT

As the size and complexity of Web sites expands dramatically, it has become increasingly challenging to design Web sites where Web surfers can easily find the information they seek. In this article, we address the design of the portal page of a Web site, which serves as the homepage of a Web site or a default Web portal. We define an important research problem---hyperlink selection: selecting from a large set of hyperlinks in a given Web site, a limited number of hyperlinks for inclusion in a portal page. The objective of hyperlink selection is to maximize the efficiency, effectiveness, and usage of a Web site's portal page. We propose a heuristic approach to hyperlink selection, LinkSelector, which is based on relationships among hyperlinks---structural relationships that can be extracted from an existing Web site and access relationships that can be discovered from a Web log. We compared the performance of LinkSelector with that of the current practice of hyperlink selection (i.e., manual hyperlink selection by domain experts), using data obtained from the University of Arizona Web site. Results showed that LinkSelector outperformed the current manual selection method.

REFERENCES

• 1: Anderson, C., Domingos, P., and Weld, D. 2001. Adaptive Web navigation for wireless devices. In Proceedings of the 17th International Joint Conference on Artificial Intelligence, Seattle, WA, 879--884.
• 2: Armstrong, R., Freitag, D., Joachims, T., and Mitchell, T. 1995. WebWatcher: a learning apprentice for the World Wide Web. In Proceedings of the AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments, Stanford, CA, March.
• 3: Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
• 4: Marko Balabanović , Yoav Shoham, Fab: content-based, collaborative recommendation, Communications of the ACM, v.40 n.3, p.66-72, March 1997 [doi>10.1145/245108.245124]
• 5: Sergey Brin , Lawrence Page, The anatomy of a large-scale hypertextual Web search engine, Proceedings of the seventh international conference on World Wide Web 7, p.107-117, April 1998, Brisbane, Australia
• 6: Lara D. Catledge , James E. Pitkow, Characterizing browsing strategies in the World-Wide Web, Computer Networks and ISDN Systems, v.27 n.6, p.1065-1073, April 1995
• 7: Soumen Chakrabarti , Byron E. Dom , S. Ravi Kumar , Prabhakar Raghavan , Sridhar Rajagopalan , Andrew Tomkins , David Gibson , Jon Kleinberg, Mining the Web's Link Structure, Computer, v.32 n.8, p.60-67, August 1999 [doi>10.1109/2.781636 ]
• 8: Soumen Chakrabarti, Data mining for hypertext: a tutorial survey, ACM SIGKDD Explorations Newsletter, v.1 n.2, January 2000 [doi>10.1145/846183.846187]
• 9: Data mining for path traversal patterns in a web environment, Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96), p.385, May 27-30, 1996
• 10: Web Mining: Information and Pattern Discovery on the World Wide Web, Proceedings of the 9th International Conference on Tools with Artificial Intelligence, p.558, November 03-08, 1997
• 11: Cooley, R., Tan, P., and Srivastava, J. 1999a. WebSIFT: the Web site information filter system. In Proceedings of the Web Usage Analysis and User Profiling Workshop, San Diego, CA, August.
• 12: Cooley, R., Mobasher, B., and Srivastava, J. 1999b. Data preparation for mining World Wide Web browsing patterns. Knowl. Info. Syst. 1, 1, 1--27.
• 13: Jon M. Kleinberg, Authoritative sources in a hyperlinked environment, Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms, p.668-677, January 25-27, 1998, San Francisco, California, United States
• 14: Raymond Kosala , Hendrik Blockeel, Web mining research: a survey, ACM SIGKDD Explorations Newsletter, v.2 n.1, p.1-15, June, 2000 [doi>10.1145/360402.360406]
• 15: Lang, K. 1995. NewsWeeder: Learning to filter netnews. In Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, CA.
• 16: Lawrence, S. and Giles, C. L. 1998. Searching the World Wide Web. Science 280, 98--100.
• 17: Lawrence, S. and Giles, C. L. 1999. Accessibility of information on the Web. Nature 400, 107--109.
• 18: Juhnyoung Lee , Mark Podlaseck , Edith Schonberg , Robert Hoch, Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising, Data Mining and Knowledge Discovery, v.5 n.1-2, p.59-84, January-April 2001
• 19: Yanhong Li, Toward a Qualitative Search Engine, IEEE Internet Computing, v.2 n.4, p.24-29, July 1998 [doi>10.1109/4236.707687 ]
• 20: Lieberman, H. 1995. Letizia: An agent that assists web browsing. In Proceedings of the 1995 International Joint Conference on Artificial Intelligence, Quebec, Canada, August.
• 21: Udi Manber , Ash Patel , John Robison, Experience with personalization of Yahoo!, Communications of the ACM, v.43 n.8, p.35-39, Aug. 2000 [doi>10.1145/345124.345136]
• 22: Mladenic, D. 1999. Machine learning used by personal WebWatcher. In Proceedings of ACAI-99 Workshop on Machine Learning and Intelligent Agents, Chania, Greece, July.
• 23: Bamshad Mobasher , Robert Cooley , Jaideep Srivastava, Automatic personalization based on Web usage mining, Communications of the ACM, v.43 n.8, p.142-151, Aug. 2000 [doi>10.1145/345124.345169]
• 24: Jakob Nielsen, User interface directions for the Web, Communications of the ACM, v.42 n.1, p.65-72, Jan. 1999 [doi>10.1145/291469.291470]
• 25: Jakob Nielsen , Annette Wagner, User interface design for the WWW, Conference companion on Human factors in computing systems: common ground, p.330-331, April 13-18, 1996, Vancouver, British Columbia, Canada [doi>10.1145/257089.257353]
• 26: Mike Perkowitz , Oren Etzioni, Towards adaptive Web sites: conceptual framework and case study, Artificial Intelligence, v.118 n.1-2, p.245-275, April 2000 [doi>10.1016/S0004-3702(99)00098-3 ]
• 27: Paul Resnick , Neophytos Iacovou , Mitesh Suchak , Peter Bergstrom , John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, United States [doi>10.1145/192844.192905]
• 28: Gerard. Salton, Automatic Information Organization and Retrieval., McGraw Hill Text, 1968
• 29: Upendra Shardanand , Pattie Maes, Social information filtering: algorithms for automating “word of mouth”, Proceedings of the SIGCHI conference on Human factors in computing systems, p.210-217, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223931]
• 30: Myra Spiliopoulou , Carsten Pohle, Data Mining for Measuring and Improving the Success of Web Sites, Data Mining and Knowledge Discovery, v.5 n.1-2, p.85-114, January-April 2001
• 31: Jaideep Srivastava , Robert Cooley , Mukund Deshpande , Pang-Ning Tan, Web usage mining: discovery and applications of usage patterns from Web data, ACM SIGKDD Explorations Newsletter, v.1 n.2, January 2000 [doi>10.1145/846183.846188]
• 32: Tak Woon Yan , Matthew Jacobsen , Hector Garcia-Molina , Umeshwar Dayal, From user access patterns to dynamic hypertext linking, Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems, p.1007-1014, May 1996, Paris, France

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms

Keywords:
Web mining