Metasearch and data-fusion techniques combine the rank lists of multiple document retrieval systems with the aim of improving search coverage and precision. We propose a new fusion method that partitions the rank lists of document retrieval systems into chunks. The size of chunks grows exponentially in the rank list. Using a small number of training queries, the probabilities of relevance of documents in different chunks are approximated for each search system. The estimated probabilities and normalized document scores are used to compute the final document ranks in the merged list. We show that our proposed method produces higher average precision values than previous systems across a range of testbeds. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Shokouhi, M. (2007). Segmentation of search engine results for effective data-fusion. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4425 LNCS, pp. 185–197). Springer Verlag. https://doi.org/10.1007/978-3-540-71496-5_19
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