Tuning before feedback: combining ranking discovery and blind feedback for robust retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Intelligent GP fusion from multiple sources for text classification
Proceedings of the 14th ACM international conference on Information and knowledge management
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
FPGA Acceleration of RankBoost in Web Search Engines
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
LePrEF: Learn to precompute evidence fusion for efficient query evaluation
Journal of the American Society for Information Science and Technology
Exploit semantic information for category annotation recommendation in wikipedia
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
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Web search engines have become indispensable in our daily life to help us find the information we need. Although search engines are very fast in search response time, their effectiveness in finding useful and relevant documents at the top of the search hit list needs to be improved. In this paper, we report our experience applying Genetic Programming (GP) to the ranking function discovery problem leveraging the structural information of HTML documents. Our empirical experiments using the web track data from recent TREC conferences show that we can discover better ranking functions than existing well-known ranking strategies from IR, such as Okapi, Pt.df. The performance is even comparable to those obtained by Support Vector Machine.