Personalized Web Search for Improving Retrieval Effectiveness

IEEE Transactions on Knowledge and Data Engineering

Volumn 16, Issue 1, 2004, Pages 28-40

  Liu, Fang ^a   Yu, Clement ^a   Meng, Weiyi ^b  

^a University of Illinois at Chicago   (United States)

^b State University of New York at Binghamton   (United States)

Author keywords

Category hierarchy; Information filtering; Personalization; Retrieval effectiveness; Search engine

Indexed keywords

CATEGORY HIERARCHY; INFORMATION FILTERING; PERSONALIZATION; RETRIEVAL EFFECTIVENESS;

ALGORITHMS; INFORMATION RETRIEVAL; PROBLEM SOLVING; QUERY LANGUAGES; SEARCH ENGINES;

COMPUTER SCIENCE;

EID: 0742268828     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2004.1264820     Document Type: Article

References (41)

1. J. Allan, "Incremental Relevance Feedback for Information Filtering," Proc. 19th Ann. Int'l ACM SICIR Conf. Research and Development in Information Retrieval, pp. 270-278, 1996.
2. M. Balabanovic and Y. Shoham, "Learning Information Retrieval Agents: Experiments with Automated Web Browsing," Proc. On-Line Working Notes AAAI Spring Symp. Series on Information Gathering from Distributed, Heterogeneous Environments, pp. 13-18, 1995.
3. K. Bollacker, S. Lawrence, and C.L. Giles, "A System for Automatic Personalized Tracking of Scientific Literature on the Web," Proc. Fourth ACM Conf. Digital Libraries, pp. 105-113, 1999.
4. J. Budzik and K. Hammond, "Watson: Anticipating and Contextualizing Information Needs," Proc. 62nd Ann. Meeting Am. Soc. Information Science, 1999.
5. U. Cetintemel, M.J. Franklin, and C.L. Giles, "Self-Adaptive User Profiles for Large-Scale Data Delivery," Proc. 16th Int'l Conf. Data Eng. (ICDE), pp. 622-633, 2000.
6. L. Chen and K. Sycara, "WebMate: A Personal Agent for Browsing and Searching," Proc. Second Int'l Conf. Autonomous Agents and Multi Agent Systems, pp. 132-139, 1998.
7. S. Deerwester, S.T. Dumais, G.W. Furnas, T.K. Landauer, and R. Harshman, "Indexing by Latent Semantic Analysis," J. Am. Soc. Information Science (JASIS), vol. 41, no. 6, pp. 391-407, 1990.
8. R. Dolin, D. Agrawal, A. El Abbadi, and J. Pearlman, "Using Automated Classification for Summarizating and Selecting Heterogeneous Information Sources," D-Lib Magazine, 1998.
9. C. Dwork, R. Kumar, M. Naor, and D. Sivakumar, "Rank Aggregation Methods for the Web," Proc. 10th Int'l World Wide Web Conf., pp. 613-622, 2001.
10. P.W. Foltz and S.T. Dumais, "Personalized Information Delivery: An Analysis of Information Filtering Methods," Comm. ACM, vol. 35, no. 12, pp. 51-60, 1992.
11. W.B. Frakes and R. Baeza-Yates, Information Retrieval: Data Structures and Algorithms. Prentice Hall, 1992.
12. N. Fuhr, "A Decision-Theoretic Approach to Database Selection in Networked IR," ACM Trans. Information Systems (TOIS), vol. 17, no. 3, pp. 229-249, 1999.
13. S. Gauch, G. Wang, and M. Gomez, "ProFusion: Intelligent Fusion from Multiple, Distributed Search Engines," J. Universal Computer Science, vol. 2, no. 9, pp. 637-649, 1996.
14. E.J. Glover, G.W. Flake, S. Lawrence, W.P. Birmingham, A. Kruger, C.L. Giles, and D.M. Pennock, "Improving Category Specific Web Search by Learning Query Modifications," SAINT, pp. 23-34, 2001.
15. G.H. Golub and C.F. Van Loan, Matrix Computations, third ed. 1996.
16. L. Gravano and H. Garcia-Molina, "Generalizing GlOSS to Vector-Space Databases and Broker Hierarchies," Proc. 21st Int'l Conf. Very Large Databases (VLDB), pp. 78-89, 1995.
17. D.A. Grossman and O. Frieder, Information Retrieval: Algorithms and Heuristics. 1998.
18. A.E. Howe and D. Dreilinger, "SavvySearch: A Meta-Search Engine that Learns which Search Engines to Query," AI Magazine, vol. 18, no. 2, pp. 19-25, 1997.
19. P. Ipeirotis, L. Gravano, and M. Sahami, "Probe, Count, and Classify: Categorizing Hidden Web Databases," ACM SIGMOD, pp. 67-78, 2001.
20. T. Joachims, D. Freitag, and T. Mitchell, "Webwatcher: A Tour Guide for the World Wide Web," Proc. 15th Int'l Joint Conf. Artificial Intelligence (IJCAI), pp. 770-777, 1997.
21. D. Koller and M. Sahami, "Hierarchically Classifying Documents Using Very Few Words," Proc. 14th Int'l Conf. Machine Learning (ICML), pp. 170-178, 1997.
22. Y. Labrou and T. Finin, "Yahoo! as an Ontology: Using Yahoo! Categories to Describe Documents," Proc. Eighth ACM Int'l Conf. Information and Knowledge Management (CIKM), pp. 180-187, 1999.
23. H. Lieberman, "Letizia: An Agent that Assists Web Browsing," Proc. 14th Int'l Joint Conf. Artificial Intelligence (IJCAI), pp. 924-929, 1995.
24. W. Meng, W. Wang, H. Sun, and C. Yu, "Concept Hierarchy Based Text Database Categorization," Int'l J. Knowledge and Information Systems, pp. 132-150, Mar. 2002.
25. T. Mitchell, Machine Learning. McGraw Hill, 1997.
26. M. Montague and J.A. Aslam, "Condorcet Fusion for Improved Retrieval," Proc. 11th ACM Int'l Conf. Information and Knowledge Management (CIKM), pp. 538-548, 2002.
27. M. Pazzani and D. Billsus, "Learning and Revising User Profiles: The Identification of Interesting Web Sites," Machine Learning, vol. 27, pp. 313-331, 1997.
28. A.L. Powell, J.C. French, J.P. Callan, M.E. Connell, and C.L. Viles, "The Impact of Database Selection on Distributed Searching," Proc. 23rd Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 232-239, 2000.
29. A. Pretschner and S. Gauch, "Ontology Based Personalized Search," Proc. Eighth IEEE Int'l Conf. Tools with Artificial Intelligence (ICTAI), pp. 391-198, 1999.
30. J. Rocchio, "Relevance Feedback in Information Retrieval," The Smart Retrieval System: Experiments in Automatic Document Processing, pp. 313-323, 1971.
31. S.E. Robertson and I. Soboroff, "The TREC-10 Filtering Track Report," Proc. Text REtrieval Conf. (TREC-10), 2001, http://trec.nist.gov/pubs/trec10/papers/filtering_track.pdf.
32. G. Salton and M. McGill, Introduction to Modern Information Retrieval. New York: McGraw-Hill, 1983.
33. E.M. Voorhees, "Overview of TREC 2001," Proc. Text REtrieval Conf. (TREC-10), 2001, http://trec.nist.gov/pubs/trec10/papers/overview_10.pdf.
34. "Common Evaluation Measures," Proc. Text REtrieval Conf. (TREC-10), E.M. Voorhees and D. Harman, eds., p. A-14, 2001.
35. D.H. Widyantoro, T.R. Ioerger, and J. Yen, "An Adaptive. Algorithm for Learning Changes in User Interests," Proc. Eighth ACM Int'l Conf. Information and Knowledge Management (CIKM), pp. 405-412, 1999.
36. J. Xu and W.B. Croft, "Cluster-Based Language Models for Distributed Retrieval," Proc. 22nd Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), pp. 254-261, 1999.
37. T.W. Yan and H. Garcia-Molina, "SIFT - A Tool for Wide-Area Information Dissemination," Proc. 1995 USENIX Technical Conf., pp. 177-186, 1995.
38. Y. Yang and C.G. Chute, "An Example-Based Mapping Method for Text Categorization and Retrieval," ACM Trans. Information Systems (TOIS), vol. 12, no. 3, pp. 252-277, 1994.
39. Y. Yang, "Noise Reduction in a Statistical Approach to Text Categorization," Proc. 18th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), pp. 256-263, 1995.
40. Y. Yang and X. Liu, "A Re-Examination of Text Categorization Methods," Proc. 22nd Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), pp. 42-49, 1999.
41. C.T. Yu, W. Meng, W. Wu, and K.-L. Liu, "Efficient and Effective Metasearch for Text Databases Incorporating Linkages among Documents," ACM SIGMOD, pp. 187-198, 2001.