PVA: A Self-Adaptive Personal View Agent

  • Chien Chin Chen
  • Meng Chang Chen
  • Yeali Sun
Article

Abstract

In this paper, we present PVA, an adaptive personal view information agent system for tracking, learning and managing user interests in Internet documents. PVA consists of three parts: a proxy, personal view constructor, and personal view maintainer. The proxy logs the user's activities and extracts the user's interests without user intervention. The personal view constructor mines user interests and maps them to a class hierarchy (i.e., personal view). The personal view maintainer synchronizes user interests and the personal view periodically. When user interests change, in PVA, not only the contents, but also the structure of the user profile are modified to adapt to the changes. In addition, PVA considers the aging problem of user interests. The experimental results show that modulating the structure of the user profile increases the accuracy of a personalization system.

machine learning automatic classification personalization agent WWW 

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References

  1. Billsus, D. and Pazzani, M.J. (1999). A Personal News Agent that Talks, Learns and Explains. In Proceedings of the Third International Conference on Autonomous Agents, Seattle, WA (pp. 268–275).Google Scholar
  2. Chan, P.K. (1999). A Non-Invasive Learning Approach to BuildingWeb User Profiles. In Proceedings of KDD-99 Workshop on Web Usage Analysis and User Profiling, San Diego, CA (pp. 7–12).Google Scholar
  3. Chen, H. and Dumais, S. (2000). Bringing Order to the Web: Automatically Categorizing Search Results. In Proceedings of the CHI 2000 Conference on Human Factors in Computing Systems, Seattle,WA (pp. 145–152).Google Scholar
  4. Chen, L. and Sycara, K. (1998). WebMate: A Personal Agent for Browsing and Searching. In Proceedings of the Second International Conference on Autonomous Agents, Minneapolis, MN (pp. 132–139).Google Scholar
  5. Goecks, J. and Shavlik, J. (2000). Learning Users' Interests by Unobtrusively Observing Their Normal Behavior. In Proceedings of the 2000 International Conference on Intelligent User Interfaces, New Orleans, LA (pp. 129–132).Google Scholar
  6. Han, E.H., Boley, D., Gini, M., Gross, R., Hastings, K., Karypis, G., Kumar, V., Mobasher, B., and Moore, J. (1998). WebACE: A Web Agent for Document Categorization and Exploration. In Proceedings of the Second International Conference on Autonomous Agents, Minneapolis, MN (pp. 408–415).Google Scholar
  7. Hijikata, Y. (1999). Estimating a User's Degree of Interest in a Page during Web Browsing. In Proceedings of IEEE Systems, Man, and Cybernetics, Tokyo, Japan (pp. 105–110).Google Scholar
  8. Hoashi, K., Matsumoto, K., Inoue, N., and Hashimoto, K. (2000). Document Filtering Method Using Non-Relevant Information Profile. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Athens, Greece (pp. 176–183).Google Scholar
  9. Klinkenberg, R. and Renz, I. (1998). Adaptive Information Filtering: Learning Drifting Concepts. In AAAI98/ICML-98 Workshop Learning for Text Categorization.Google Scholar
  10. Korfhage, R.R. (1997). Information Storage and Retrieval. New York, NY: Wiley Computer Publishing.Google Scholar
  11. Li, W.S., Vu, Q., Chang, E., Agrawal, D., Hirata, K., Mukherjea, S., Wu, Y.L., Bufi, C., Chang, C.C.K., Hara, Y., Ito, R., Kimura, Y., Shimazu, K., and Saito, Y. (1999). PowerBookmarks: A System for Personalizable Web Information Organization, Sharing, and Management. In Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, Philadelphia, PA (pp. 565–567).Google Scholar
  12. Lin, S.H., Shih, C.S., Chen, M.C., Ho, J.M., Ko, M.T., and Huang, Y.M. (1998). Extracting Classification Knowledge of Internet Documents with Mining Term Associations: A semantic Approach. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia (pp. 241–249).Google Scholar
  13. Lin, S.H., Shih, C.S., Chen, M.C., Ho, J.M., Ko, M.T., and Huang, Y.M. (1999). ACIRD: Intelligent Internet Documents Organization and Retrieval. Technical Report, IIS, Academia Sinica. Also in IEEE Transactions on Knowledge and Data Engineering.Google Scholar
  14. Menczer, F., Belew, R.K., and Willuhn, W. (1995). Artificial Life Applied to Adaptive Information Agents. In Working Notes of the AAAI Symposium on Information Gathering from Distributed, Heterogeneous Databases. Menlo Park, CA: AAAI Press.Google Scholar
  15. Mitchell, T.M. (1997). Machine Learning. Boston, MA: WCB McGraw-Hill.Google Scholar
  16. Mladenic, D. (1999). Machine Learning Used by PersonalWebWatcher. In Proceedings of ACAI-99 Workshop on Machine Learning and Intelligent Agents, Chania, Greece.Google Scholar
  17. Mobasher, B., Dai, H., Luo, T., Nakagawa, M., Sun, Y., and Wiltshire, S. (2000a). Discovery of Aggregate Usage Profiles for Web Personalization. In Proceedings of the Web Mining for E-Commerce Workshop, Boston, MA.Google Scholar
  18. Mobasher, B., Cooley, R., and Srivastava, J. (2000b). Automatic Personalization Based on Web Usage Mining. Communications of the ACM, 43(8), 142–151.Google Scholar
  19. Pretschner, A. and Gauch, S. (1999a). Personalization on theWeb. Technical Report ITTC-FY2000-TR-13591-01, Information and Telecommunication Technology Center (ITTC), The University of Kansas, Lawrence, KS.Google Scholar
  20. Pretschner, A. and Gauch, S. (1999b). Ontology Based Personalized Search. In Proceedings of 11th IEEE International Conference On Tools with Artificial Intelligence, Chicago, IL (pp. 391–398).Google Scholar
  21. Salton, G. (1989). Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Reading, MA: Addison-Wesley.Google Scholar
  22. Widyantoro, D.H., Ioerger, T.R., and Yen, J. (1999). AnAdaptive Algorithm for Learning Changes in User Interests. In Proceedings of the Eighth International Conference on Information Knowledge Management, Kansas City, MO (pp. 405–412).Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Chien Chin Chen
    • 1
  • Meng Chang Chen
    • 1
  • Yeali Sun
    • 2
  1. 1.Institute of Information ScienceAcademia SinicaTaiwan
  2. 2.Department of Information ManagementNational Taiwan UniversityTaiwan

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