Merging algorithms for enterprise search

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 multi-grade 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