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We present a thorough analysis of the capabilities of the linear combination (LC) model for fusion of information retrieval systems. The LC model combines the results lists of multiple IR systems by scoring each document using a weighted sum of the scores from each of the component systems. We first present both empirical and analytical justification for(More)
We introduce a new technique for analyzing combination models. The technique allows us to make qualitative conclusions about which IR systems should be combined. We achieve this by using a linear regression to accurately (T ' = 0.98) predict the performance of the combined system based on quantitative measurements of individual component systems taken from(More)
A linear mixture of experts is used to combine three standard IR systems. The parameters for the mixture are determined automatically through training on document relevance assessments via optimization of a rank-order statistic which is empirically correlated with average precision. The mixture improves performance in some cases and degrades it in others,(More)
We present the results of some expansion experiments for solving the routing, data fusion problem using TREC5 systems. The experiments address the question " How much more is better? " when combining the results of multiple information retrieval systems using a linear combination (weighted sum) model. By investigating all 2-way, 3-way, 4-way and 10-way(More)
The concept of a " user lens " is introduced. The lens is a sequence of linear transformations used to reweight the vectors which represent documents or queries in information retrieval systems. It is trained automatically via relevance data provided by the user. Experiments verify the lens can improve performance on training data while not degrading test(More)