Personalizing Information Retrieval in a Distributed Environment
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Information personalization is the process of enhancing an information seeking process with user preferences in order to provide accurate results in response to a user query. The underlying idea is that different users have different backgrounds, goals and interests when seeking information and so a same query may cover different specific information needs according to who emitted it. In this paper, we address information personalization in a distributed environment. Most prior research in distributed information access focused on selecting and merging information that have the most relevant content according to the query but ignored the user’s specific needs. With the ever expanding Web, users are faced with a huge number of information resources; consequently, such query-based information access strategies lead to inaccurate query results. In order to tackle this problem, we propose (a) models for representing both user and information source using feature based profiles (b) algorithms for source selection and results merging that personalize the computation of the relevance score of a document in response to the user’s query. The proposed personalization approach has been experimented using a multi-agent based framework dealing with several known search engines. The experimental results obtained show the effectiveness of our approach.
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J. Callan, Z. Lu, W.B. Croft, Searching distributed collections with inference networks, ACM-SIGIR’95, 1995 pp. 21-28.
J. Xu, J.P. Callan, Effective retrieval with distributed collections. ACM SIGIR’98, 1998 pp 112-120.
L. Gravano, C. Chang, H. Garcia-Molina, A. Paepcke, STARTS: Stanford proposal for internet metasearching. Proceedings of the 20th ACM-SIGMOD International Conference on Management of Data, 1997 pp 207-218.
D. Hawking, P. Thistlewaite, Methods for information server selection. ACM Transactions on Information Systems, 1999, 17(1), pp 40-76.
N. Fuhr, A Decision-Theoretic approach to database selection in networked IR. ACM Transactions on Information Systems, 1999, 17(3). pp. 229-249.
H. Nottelmann, N. Fuhr, Evaluating different method of estimating retrieval quality for resource selection. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2003. pp290-297.
L. Si, J. Callan, (2004). Unified Utility Maximization Framework for Resource Selection. Proceedings of the ninth nternational conference on Information and knowledge management CIKM washington USA 2004, pp 32-41.
E. M. Voorhees, N. Gupta, B. Johnson-Laird. (1995). Learning Collection Fusion Strategies. Proceedings of the ACM-SIGIR'95 1995 pp 172-179.
K. L. Kwok, L. Grunfeld, D. D. Lewis, TREC-3 Ad-hoc, Routing Retrieval and Thresholding Experiments using PIRCS. Proceedings of TREC-3,1995, pp. 247-255.
L.S. Larkey, M.E. Connell, J. Callan, Collection and Results Merging with Topically Organized U.S. Patents and TREC Data. Proceedings of CIK 2000. pp.282-289.
L. Si, J. Callan, (2003). A Semi-Supervised learning method to merge search engine results. ACM Transactions on Information Systems.2003 pp. 457-491.
J. Lu & J. Callan, User modeling for full-text federated search in peer-topeer networks. Proceedings of the Twenty Eigth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Seattle, USA: ACM 2006, pp 83-90, pp332-339.
G. Salton, A. Wong, C. Yang, (1975) A vector space model for automatic indexing. Communications of the ACM, 18, 1975 pp. 613-620.
G. Nunberg, As Google goes, so goes the nation, New York times, May 2003.
J. P Mc Gowan, A multiple model approach to personalised information access. Master Thesis in computer science, Faculty of science, University College Dublin 2003.
J. Rocchio, J. (1971). Relevance feedback information retrieval. In Gerald Salton (editor), The SMART retrieval system- experiments in automated document processing. Prentice-Hall, Englewood Cliffs, NJ, 1971. pp 313-323.
D. Kelly, J. Teevan, Implicit feedback for inferring user preference: A bibliography. SIGIR Forum, 2003, pp 18-28.
M. Pazzani, D. Billsus, Learning and revising user profiles: The identification of interesting Web sites, Machine learning, Vol 27, 1997 pp 313-331
E.A. Fox, J.A. Shaw, Combination of multiple Searches. In the Second text Retrieval Conference (TREC-2), U.S. Government Printing Office Gaithersburg, Maryland, USA. 1993.
H. Lieberman, Letizia An agent that assists web browsing. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’95), Montreal, August 1995, pp 924-929.
F. Liu, C. Yu, Personalized Web search for improving retrieval effectiveness. IEEE Transactions on knowledge and data engineering, 16(1). 2004 pp 28-40.
J. Budzik, K.J Hammond, (2000). Users interactions with everyday applications as context for just-in-time information access. In Proceedings of the 5th international conference on intelligent user interfaces ,2000 pp 44-51.
L. Chen, K. Sycara, WebMate: A Personal Agent for Browsing and Searching, In Proceedings of the 2nd International Conference on Autonomous Agents and Multi Agent Systems, Minneapolis, MN, May 10-13. 1998 pp 132-139.
Y-W. Seo, T. Zhang, (2000). A Reinforcement Learning Agent for Personalized Information Filtering. Proceedings of the 2000 International Conference on Intelligent User Interfaces, New-Orleans, USA ACM 2000 pp 248-251.
D. Bertolini, P. Busetta, A. Molani, M. Nori, A. Perini, Designing peer-to-peer applications: an agent-oriented approach. Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, 2003.
H. Zhang, W.B. Croft, B. Levine, V. Lesser. A Multi-agent Approach for Peer-to-Peer-based Information Retrieval Systems AAMAS'04, July 19-23, 2004, New York, New York, USA.Copyright 2004 pp 456-464, ACM 1-58113-864-4/04/0007.
L. Si, J. Callan, (2005). Modeling Search Engine Effectiveness for Federated Search. Proceedings of the Twenty Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Toronto, Canada: ACM 2005, pp 83-90.
E. A. Fox, J.A. Shaw, Combination of multiple Searches. In the Second text Retrieval Conference (TREC-2), U.S. Government Printing Office Gaithersburg, Maryland, USA. 1993.
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