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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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Patent
Wen-Syan Li1, Kasim Selouk Candan1
29 Aug 1997
TL;DR: In this article, a computer implemented method for searching and retrieving images contained within a database of images in which both semantic and cognitive methodologies are utilized is presented, and successively refines the search utilizing semantic and Cognitive methodologies and then ranking the results for presentation to the user.
Abstract: A computer implemented method for searching and retrieving images contained within a database of images in which both semantic and cognitive methodologies are utilized. The method accepts a semantic and cognitive description of an image to be searched from a user, and successively refines the search utilizing semantic and cognitive methodologies and then ranking the results for presentation to the user.

227 citations

Proceedings ArticleDOI
06 Jun 2011
TL;DR: This is the first analysis to show an algorithm which breaks the natural 1 - 1/e -barrier' in the unknown distribution model (the authors' analysis in fact works in the stricter, random order model) and answers an open question in [GM08].
Abstract: We consider the online bipartite matching problem in the unknown distribution input model. We show that the Ranking algorithm of [KVV90] achieves a competitive ratio of at least 0.653. This is the first analysis to show an algorithm which breaks the natural 1 - 1/e -barrier' in the unknown distribution model (our analysis in fact works in the stricter, random order model) and answers an open question in [GM08]. We also describe a family of graphs on which Ranking does no better than 0.727 in the random order model. Finally, we show that for graphs which have k > 1 disjoint perfect matchings, Ranking achieves a competitive ratio of at least 1 - √(1/k - 1/k2 + 1/n) -- in particular Ranking achieves a factor of 1 - o(1) for graphs with ω(1) disjoint perfect matchings.

226 citations

Journal ArticleDOI
Olivier Chapelle1, Mingrui Wu1
TL;DR: This work proposes an algorithm which aims at directly optimizing popular measures such as the Normalized Discounted Cumulative Gain and the Average Precision, to minimize a smooth approximation of these measures with gradient descent.
Abstract: Most ranking algorithms are based on the optimization of some loss functions, such as the pairwise loss. However, these loss functions are often different from the criteria that are adopted to measure the quality of the web page ranking results. To overcome this problem, we propose an algorithm which aims at directly optimizing popular measures such as the Normalized Discounted Cumulative Gain and the Average Precision. The basic idea is to minimize a smooth approximation of these measures with gradient descent. Crucial to this kind of approach is the choice of the smoothing factor. We provide various theoretical analysis on that choice and propose an annealing algorithm to iteratively minimize a less and less smoothed approximation of the measure of interest. Results on the Letor benchmark datasets show that the proposed algorithm achieves state-of-the-art performances.

226 citations

Patent
09 Apr 2009
TL;DR: In this paper, the sentimental significance of a group of historical documents related to a topic is assessed with respect to change in an extrinsic metric for the topic and a unique sentiment binding label is included to the content of actions documents that are determined to have sentimental significance.
Abstract: The sentimental significance of a group of historical documents related to a topic is assessed with respect to change in an extrinsic metric for the topic. A unique sentiment binding label is included to the content of actions documents that are determined to have sentimental significance and the group of documents is inserted into a historical document sentiment vector space for the topic. Action areas in the vector space are defined from the locations of action documents and singular sentiment vector may be created that describes the cumulative action area. Newly published documents are sentiment-scored by semantically comparing them to documents in the space and/or to the singular sentiment vector. The sentiment scores for the newly published documents are supplemented by human sentiment assessment of the documents and a sentiment time decay factor is applied to the supplemented sentiment score of each newly published documents. User queries are received and a set of sentiment-ranked documents is returned with the highest age-adjusted sentiment scores.

226 citations

Journal ArticleDOI
01 Apr 1998
TL;DR: A server that provides linkage information for all pages indexed by the AltaVista search engine and can produce the entire neighbourhood of L up to a given distance, and envisage numerous other applications such as ranking, visualization, and classification.
Abstract: We have built a server that provides linkage information for all pages indexed by the AltaVista search engine. In its basic operation, the server accepts a query consisting of a set L of one or more URLs and returns a list of all pages that point to pages in L (predecessors) and a list of all pages that are pointed to from pages in L (successors). More generally the server can produce the entire neighbourhood (in the graph theory sense) of L up to a given distance and can include information about all links that exist among pages in the neighbourhood. Although some of this information can be retrieved directly from Alta Vista or other search engines, these engines are not optimized for this purpose and the process of constructing the neighbourhood of a given set of pages is show and laborious. In contrast our prototype server needs less than 0.1 ms per result URL. So far we have built two applications that use the Connectivity Server: a direct interface that permits fast navigation of the Web via the predecessor/successor relation, and a visualization tool for the neighbourhood of a given set of pages. We envisage numerous other applications such as ranking, visualization, and classification.

225 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