We present a new algorithm for improving retrieval results by combining document ranking functions: Condorcet-fuse. Beginning with one of the two major classes of voting procedures from Social Choice Theory, the Condorcet procedure, we apply a graph-theoretic analysis that yields a sorting-based algorithm that is elegant, efficient, and effective. The algorithm performs very well on TREC data, often outperforming existing metasearch algorithms whether or not relevance scores and training data is available. Condorcet-fuse significantly outperforms Borda-fuse, the analogous representative from the other major class of voting algorithms.

Given the ranked lists of documents returned by multiple search engines in response to a given query, the problem of metasearch is to combine these lists in a way which optimizes the performance of the combination. This problem can be naturally decomposed into three subproblems: (1) normalizing the relevance scores given by the input systems, (2) estimating relevance scores for unretrieved documents, and (3) combining the newly-acquired scores for each document into one, improved score.Research on the problem of metasearch has historically concentrated on algorithms for combining (normalized) scores. In this paper, we show that the techniques used for normalizing relevance scores and estimating the relevance scores of unretrieved documents can have a significant effect on the overall performance of metasearch. We propose two new normalization/estimation techniques and demonstrate empirically that the performance of well known metasearch algorithms can be significantly improved through their use.

Given the ranked lists of documents returned by multiple search engines in response to a given query, the problem ofmetasearchis to combine these lists in a way which optimizes the performance of the combination. This paper makes three contributions to the problem of metasearch: (1) We describe and investigate a metasearch model based on an optimal democratic voting procedure, the Borda Count; (2) we describe and investigate a metasearch model based on Bayesian inference; and (3) we describe and investigate a model for obtaining upper bounds on the performance of metasearch algorithms. Our experimental results show that metasearch algorithms based on the Borda and Bayesian models usually outperform the best input system and are competitive with, and often outperform, existing metasearch strategies. Finally, our initial upper bounds demonstrate that there is much to learn about the limits of the performance of metasearch.

We investigate the performance of metasearch algorithms in terms of how much they improve consistency. We find that three different metasearch algorithms, each over three datasets, usually improve the consistency of search results; sometimes the improvement is dramatic. Furthermore, consistency tends to improve when performance improves.

We introduce a new, probabilistic model for combining the outputs of an arbitrary number of query retrieval systems. By gathering simple statistics on the average performance of a given set of query retrieval systems, we construct a Bayes optimal mechanism for combining the outputs of these systems. Our construction yields a metasearch strategy whose empirical performance nearly always exceeds the performance of any of the constituent systems. Our construction is also robust in the sense that if “good” and “bad” systems are combined, the Performance of the composite is still on par with, or exceeds, that of the best constituent system. Finally, our model and theory provide theoretical and empirical avenues for the improvement of this metasearch strategy.

Article A (usually?) connected subgraph of the minimum weight triangulation Share on Authors: Matthew T. Dickerson Middlebury College, Middlebury VT Middlebury College, Middlebury VTView Profile , Mark H. Montague Middlebury College, Middlebury VT and now at Dartmouth College, Hanover NH Middlebury College, Middlebury VT and now at Dartmouth College, Hanover NHView Profile Authors Info & Claims SCG '96: Proceedings of the twelfth annual symposium on Computational geometryMay 1996 Pages 204–213https://doi.org/10.1145/237218.237364Online:01 May 1996Publication History 18citation370DownloadsMetricsTotal Citations18Total Downloads370Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Article Free Access Share on New algorithms and empirical findings on minimum weight triangulation heuristics (extended abstract) Authors: Matthew T. Dickerson Middlebury College, Middlebury VT Middlebury College, Middlebury VTView Profile , Scott A. McElfresh Dartmouth College, Hanover NH Dartmouth College, Hanover NHView Profile , Mark Montague Middlebury College, Middlebury VT Middlebury College, Middlebury VTView Profile Authors Info & Claims SCG '95: Proceedings of the eleventh annual symposium on Computational geometrySeptember 1995 Pages 238–247https://doi.org/10.1145/220279.220305Online:01 September 1995Publication History 10citation334DownloadsMetricsTotal Citations10Total Downloads334Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF