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Discovery of Context-Specific Ranking Functions for Effective Information Retrieval Using Genetic Programming
April 2004 (vol. 16 no. 4)
pp. 523-527

Abstract—The Internet and corporate Intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. In this paper, we argue that the ranking strategy should be context specific and we propose a new systematic method that can automatically generate ranking strategies for different contexts based on Genetic Programming (GP). The new method was tested on TREC data and the results are very promising.

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Index Terms:
Intelligent information retrieval, personalization, search engine, term weighting, ranking function, text mining, genetic programming, contextual information retrieval, information routing, information retrieval.
Citation:
Weiguo Fan, Michael D. Gordon, Praveen Pathak, "Discovery of Context-Specific Ranking Functions for Effective Information Retrieval Using Genetic Programming," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 4, pp. 523-527, April 2004, doi:10.1109/TKDE.2004.1269663
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