Abstract
This paper describes a new algorithm for merging the results of remote collections in a distributed information retrieval environment. The algorithm makes use only of the ranks of the returned documents, thus making it very efficient in environments where the remote collections provide the minimum of cooperation. Assuming that the correlation between the ranks and the relevancy scores can be expressed through a logistic function and using sampled documents from the remote collections the algorithm assigns local scores to the returned ranked documents. Subsequently, using a centralized sample collection and through linear regression, it assigns global scores, thus producing a final merged document list for the user. The algorithm’s effectiveness is measured against two state-of-the-art results merging algorithms and its performance is found to be superior to them in environments where the remote collections do not provide relevancy scores.
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Paltoglou, G., Salampasis, M., Satratzemi, M. (2007). Results Merging Algorithm Using Multiple Regression Models. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_18
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DOI: https://doi.org/10.1007/978-3-540-71496-5_18
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