Merging algorithms for enterprise search

Peng Fei Li, Paul Thomas, David Hawking

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)


Effective enterprise search must draw on a number of sources-for example web pages, telephone directories, and databases. Doing this means we need a way to make a single sorted list from results of very different types. Many merging algorithms have been proposed but none have been applied to this, realistic, application. We report the results of an experiment which simulates heterogeneous enterprise retrieval, in a university setting, and uses multigrade expert judgements to compare merging algorithms. Merging algorithms considered include several variants of round-robin, several methods proposed by Rasolofo et al. in the Current News Metasearcher, and four novel variations including a learned multi-weight method. We find that the round-robin methods and one of the Rasolofo methods perform significantly worse than others. The GDS TS method of Rasolofo achieves the highest average NDCG@101 score but the differences between it and the other GDS methods, local reranking, and the multi-weight method were not significant. Copyright is held by the owner/author(s).

Original languageEnglish
Title of host publicationADCS 2013 - Proceedings of the 18th Australasian Document Computing Symposium
Number of pages8
Publication statusPublished - 2013
Event18th Australasian Document Computing Symposium, ADCS 2013 - Brisbane, QLD, Australia
Duration: 5 Dec 20136 Dec 2013

Publication series

NameACM International Conference Proceeding Series


Conference18th Australasian Document Computing Symposium, ADCS 2013
CityBrisbane, QLD


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