This Article 
 Bibliographic References 
 Add to: 
Personalized Web Search For Improving Retrieval Effectiveness
January 2004 (vol. 16 no. 1)
pp. 28-40

Abstract—Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.

[1] J. Allan, Incremental Relevance Feedback for Information Filtering Proc. 19th Ann. Int'l ACM SIGIR 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. Franklin, and C. Giles, Self-Adaptive User Profiles for Large-Scale Data Delivery Proc. Int'l Conf. Data Eng., 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. Glover, G. Flake, S. Lawrence, W.P. Birmingham, A. Kruger, C.L. Giles, and D. Pennock, Improving Category Specific Web Search by Learning Query Modifications Proc. 2001 Symp. Applications and the Internet (SAINT 2001) pp. 23-31, 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, papers/ieee/ieee-metacrawler.pshttp:// .
[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, .
[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.

Index Terms:
Category hierarchy, information filtering, personalization, retrieval effectiveness, search engine.
Fang Liu, Clement Yu, Weiyi Meng, "Personalized Web Search For Improving Retrieval Effectiveness," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 1, pp. 28-40, Jan. 2004, doi:10.1109/TKDE.2004.1264820
Usage of this product signifies your acceptance of the Terms of Use.