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Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.


Papers
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Journal ArticleDOI
TL;DR: The forward orthogonalization strategy, which combines good effectiveness with high efficiency, enables the new algorithm to produce efficient feature subsets with a clear physical interpretation.
Abstract: A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature selection and ranking. In the new algorithm, features are selected in a stepwise way, one at a time, by estimating the capability of each specified candidate feature subset to represent the overall features in the measurement space. A squared correlation function is employed as the criterion to measure the dependency between features and this makes the new algorithm easy to implement. The forward orthogonalization strategy, which combines good effectiveness with high efficiency, enables the new algorithm to produce efficient feature subsets with a clear physical interpretation

185 citations

Proceedings ArticleDOI
13 Jun 2004
TL;DR: This work focuses on a subclass of CAS queries consisting of simple path expressions, which study algorithmic issues in integrating structure indexes with inverted lists for the evaluation of these queries, where they rank all documents that match the query and return the top k documents in order of relevance.
Abstract: Several methods have been proposed to evaluate queries over a native XML DBMS, where the queries specify both path and keyword constraints These broadly consist of graph traversal approaches, optimized with auxiliary structures known as structure indexes; and approaches based on information-retrieval style inverted lists We propose a strategy that combines the two forms of auxiliary indexes, and a query evaluation algorithm for branching path expressions based on this strategy Our technique is general and applicable for a wide range of choices of structure indexes and inverted list join algorithms Our experiments over the Niagara XML DBMS show the benefit of integrating the two forms of indexes We also consider algorithmic issues in evaluating path expression queries when the notion of relevance ranking is incorporated By integrating the above techniques with the Threshold Algorithm proposed by Fagin et al, we obtain instance optimal algorithms to push down top k computation

185 citations

Proceedings ArticleDOI
08 May 2007
TL;DR: Experimental results on meta-searches show that Supervised Rank Aggregation can significantly outperform existing unsupervised methods and it is proved that the optimization problem can be transformed into that of Semidefinite Programming and solve it efficiently.
Abstract: This paper is concerned with rank aggregation, the task of combining the ranking results of individual rankers at meta-search. Previously, rank aggregation was performed mainly by means of unsupervised learning. To further enhance ranking accuracies, we propose employing supervised learning to perform the task, using labeled data. We refer to the approach as Supervised Rank Aggregation. We set up a general framework for conducting Supervised Rank Aggregation, in which learning is formalized an optimization which minimizes disagreements between ranking results and the labeled data. As case study, we focus on Markov Chain based rank aggregation in this paper. The optimization for Markov Chain based methods is not a convex optimization problem, however, and thus is hard to solve. We prove that we can transform the optimization problem into that of Semidefinite Programming and solve it efficiently. Experimental results on meta-searches show that Supervised Rank Aggregation can significantly outperform existing unsupervised methods.

184 citations

Journal ArticleDOI
TL;DR: A linear goal programming model is constructed to integrate the fuzzy assessment information and to directly compute the collective ranking values of alternatives without the need of information transformation to solve the group decision making (GDM) problems with multi-granularity linguistic assessment information.

184 citations

Journal ArticleDOI
01 Jun 1999
TL;DR: A theoretical and experimental analysis of the resulting search space and a novel query optimization algorithm that is designed to perform well under the different conditions that may arise are described.
Abstract: We consider the problem of query optimization in the presence of limitations on access patterns to the data (i.e., when one must provide values for one of the attributes of a relation in order to obtain tuples). We show that in the presence of limited access patterns we must search a space of annotated query plans, where the annotations describe the inputs that must be given to the plan. We describe a theoretical and experimental analysis of the resulting search space and a novel query optimization algorithm that is designed to perform well under the different conditions that may arise. The algorithm searches the set of annotated query plans, pruning invalid and non-viable plans as early as possible in the search space, and it also uses a best-first search strategy in order to produce a first complete plan early in the search. We describe experiments to illustrate the performance of our algorithm.

184 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168