Topic
Pairwise comparison
About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.
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14 Jun 2009TL;DR: An on-line learning framework tailored towards real-time learning from observed user behavior in search engines and other information retrieval systems and an algorithm with theoretical guarantees as well as simulation results are presented.
Abstract: We present an on-line learning framework tailored towards real-time learning from observed user behavior in search engines and other information retrieval systems. In particular, we only require pairwise comparisons which were shown to be reliably inferred from implicit feedback (Joachims et al., 2007; Radlinski et al., 2008b). We will present an algorithm with theoretical guarantees as well as simulation results.
351 citations
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TL;DR: This work introduces a temperature (of selection) to account for stochastic effects and calculates the fixation probabilities and fixation times for any symmetric 2 x 2 game, for any intensity of selection and any initial number of mutants.
343 citations
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TL;DR: A correspondence between dominant sets and the extrema of a quadratic form over the standard simplex is established, thereby allowing the use of straightforward and easily implementable continuous optimization techniques from evolutionary game theory.
Abstract: We develop a new graph-theoretic approach for pairwise data clustering which is motivated by the analogies between the intuitive concept of a cluster and that of a dominant set of vertices, a notion introduced here which generalizes that of a maximal complete subgraph to edge-weighted graphs. We establish a correspondence between dominant sets and the extrema of a quadratic form over the standard simplex, thereby allowing the use of straightforward and easily implementable continuous optimization techniques from evolutionary game theory. Numerical examples on various point-set and image segmentation problems confirm the potential of the proposed approach
341 citations
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04 Feb 2013TL;DR: This work proposes a new model to predict a gold-standard ranking that hinges on combining pairwise comparisons via crowdsourcing and formalizes this as an active learning strategy that incorporates an exploration-exploitation tradeoff and implements it using an efficient online Bayesian updating scheme.
Abstract: Inferring rankings over elements of a set of objects, such as documents or images, is a key learning problem for such important applications as Web search and recommender systems. Crowdsourcing services provide an inexpensive and efficient means to acquire preferences over objects via labeling by sets of annotators. We propose a new model to predict a gold-standard ranking that hinges on combining pairwise comparisons via crowdsourcing. In contrast to traditional ranking aggregation methods, the approach learns about and folds into consideration the quality of contributions of each annotator. In addition, we minimize the cost of assessment by introducing a generalization of the traditional active learning scenario to jointly select the annotator and pair to assess while taking into account the annotator quality, the uncertainty over ordering of the pair, and the current model uncertainty. We formalize this as an active learning strategy that incorporates an exploration-exploitation tradeoff and implement it using an efficient online Bayesian updating scheme. Using simulated and real-world data, we demonstrate that the active learning strategy achieves significant reductions in labeling cost while maintaining accuracy.
326 citations
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TL;DR: The GP-AHP method developed herein can concurrently tackle the pairwise comparison involving triangular, general concave and concave-convex mixed fuzzy estimates under a group decision-making environment.
315 citations