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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: It is emphasized that the use of various multiple criteria decision making methods sometimes can produce different ranking orders of alternatives, highlighted some reasons which lead to different results, and indicate that different results obtained by different MCDM methods are not just a random event, but rather reality.
Abstract: In the literature, many multiple criteria decision making methods have been proposed. There are also a number of papers, which are devoted to comparison of their characteristics and performances. However, a definitive answer to questions: which method is most suitable and which method is most effective is still actual. Therefore, in this paper, the use of some prominent multiple criteria decision making methods is considered on the example of ranking Serbian banks. The objective of this paper is not to determine which method is most appropriate for ranking banks. The objective of this paper is to emphasize that the use of various multiple criteria decision making methods sometimes can produce different ranking orders of alternatives, highlighted some reasons which lead to different results, and indicate that different results obtained by different MCDM methods are not just a random event, but rather reality.

113 citations

01 Jul 2007
TL;DR: The proposed RankGP employs genetic programming to learn a ranking function by combining various types of evidences in IR, including content features, structure features, and query-independent features, which is found to be competitive with Ranking SVM and RankBoost.
Abstract: central problem of information retrieval (IR) is to determine which documents are relevant and which are not to the user information need. This problem is practically handled by a ranking function which defines an ordering among documents according to their degree of relevance to the user query. This paper discusses work on using machine learning to automatically generate an effective ranking function for IR. This task is referred to as "learning to rank for IR" in the field. In this paper, a learning method, RankGP, is presented to address this task. RankGP employs genetic programming to learn a ranking function by combining various types of evidences in IR, including content features, structure features, and query-independent features. The proposed method is evaluated using the LETOR benchmark datasets and found to be competitive with Ranking SVM and RankBoost.

113 citations

Book ChapterDOI
09 Sep 2003
TL;DR: It is shown that order can be a natural property of the underlying data model and algebra and introduced a new query language and algebra, called AQuery, that supports order from-the-ground-up and brings orders-of-magnitude improvement over SQL:1999 systems on many natural order-dependent queries.
Abstract: An order-dependent query is one whose result (interpreted as a multiset) changes if the order of the input records is changed. In a stock-quotes database, for instance, retrieving all quotes concerning a given stock for a given day does not depend on order, because the collection of quotes does not depend on order. By contrast, finding a stock's five-price moving-average in a trades table gives a result that depends on the order of the table. Query languages based on the relational data model can handle order-dependent queries only through add-ons. SQL:1999, for instance, has a new "window" mechanism which can sort data in limited parts of a query. Add-ons make order-dependent queries di_cult to write and to optimize. In this paper we show that order can be a natural property of the underlying data model and algebra. We introduce a new query language and algebra, called AQuery, that supports order from-the-ground-up. New order-related query transformations arise in this setting. We show by experiment that this framework - language plus optimization techniques - brings orders-of-magnitude improvement over SQL:1999 systems on many natural order-dependent queries.

113 citations

Book ChapterDOI
21 Jul 2004
TL;DR: A model based on the Inference Network framework from information retrieval that employs a powerful query language that allows structured query operators, term weighting, and the combination of text and images within a query is proposed.
Abstract: Most image retrieval systems only allow a fragment of text or an example image as a query. Most users have more complex information needs that are not easily expressed in either of these forms. This paper proposes a model based on the Inference Network framework from information retrieval that employs a powerful query language that allows structured query operators, term weighting, and the combination of text and images within a query. The model uses non-parametric methods to estimate probabilities within the inference network. Image annotation and retrieval results are reported and compared against other published systems and illustrative structured and weighted query results are given to show the power of the query language. The resulting system both performs well and is robust compared to existing approaches.

112 citations

Journal ArticleDOI
TL;DR: Algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR) and a batch processing of images is proposed, leading to a fast and efficient active learning scheme to retrieve sets of online images (query concept).
Abstract: Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extension are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

112 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