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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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Proceedings ArticleDOI
Vidit Jain1, Manik Varma2
28 Mar 2011
TL;DR: This paper hypothesize that images clicked in response to a query are mostly relevant to the query, and re-rank the original search results so as to promote images that are likely to be clicked to the top of the ranked list.
Abstract: Our objective is to improve the performance of keyword based image search engines by re-ranking their original results. To this end, we address three limitations of existing search engines in this paper. First, there is no straight-forward, fully automated way of going from textual queries to visual features. Image search engines therefore primarily rely on static and textual features for ranking. Visual features are mainly used for secondary tasks such as finding similar images. Second, image rankers are trained on query-image pairs labeled with relevance judgments determined by human experts. Such labels are well known to be noisy due to various factors including ambiguous queries, unknown user intent and subjectivity in human judgments. This leads to learning a sub-optimal ranker. Finally, a static ranker is typically built to handle disparate user queries. The ranker is therefore unable to adapt its parameters to suit the query at hand which again leads to sub-optimal results. We demonstrate that all of these problems can be mitigated by employing a re-ranking algorithm that leverages aggregate user click data.We hypothesize that images clicked in response to a query are mostly relevant to the query. We therefore re-rank the original search results so as to promote images that are likely to be clicked to the top of the ranked list. Our re-ranking algorithm employs Gaussian Process regression to predict the normalized click count for each image, and combines it with the original ranking score. Our approach is shown to significantly boost the performance of the Bing image search engine on a wide range of tail queries.

152 citations

Patent
Fujun Bi, Ran Li, Shaun Bliss, Reza Nojoomi, Hong Yan 
29 Sep 1997
TL;DR: In this paper, a multi-element confidence matching system is proposed to automatically provide the user or trader with the information he is interested in without the intervention of the trader, and give the user the maximum amount of information about offers which may meet their requirement, so as to give the trader the ability to not just see offers which exactly match their criteria, but ones which come close or can fulfill part of, or more than, their needs.
Abstract: The present invention relates to a computer matching system used by a plurality of users and the method therefor, said system comprising a database; an offer creation program means for creating an entity for an offer input by each user in the database and storing said offer therein; and a search engine for comparing and matching a requirement input by a user with other users' offers stored in the database and returning matching results to said user. Advantageously, said requirement includes multiple elements as search criteria, each of said elements is assigned a weight of importance thereby each matching result has a search score indicating satisfaction level of said user, said search engine further perform ordering and ranking of said matching results according to the respective search scores thereof, and only the matching results have search scores above a predetermined satisfaction level are returned to said user. Said multi-element confidence matching system can automatically provide the user or trader with the information he is interested in without the intervention of the trader, and give the user the maximum amount of information about offers which may meet their requirement, so as to give the trader the ability to not just see offers which exactly match their criteria, but ones which come close or can fulfill part of, or more than, their needs, thereby the trader may conduct the search efficiently

152 citations

Proceedings ArticleDOI
02 Oct 2005
TL;DR: The results show that AKTiveRank will have great utility although there is potential for improvement, and a number of metrics are applied in an attempt to investigate their appropriateness for ranking ontologies.
Abstract: In view of the need to provide tools to facilitate the re-use of existing knowledge structures such as ontologies, we present in this paper a system, AKTiveRank, for the ranking of ontologies AKTiveRank uses as input the search terms provided by a knowledge engineer and, using the output of an ontology search engine, ranks the ontologies We apply a number of metrics in an attempt to investigate their appropriateness for ranking ontologies, and compare the results with a questionnaire-based human study Our results show that AKTiveRank will have great utility although there is potential for improvement

152 citations

01 Jan 2007
TL;DR: A class of simple, flexible algorithms, called LambdaRank, which avoids difficulties by working with implicit cost functions by using neural network models, and can be extended to any non-smooth and multivariate cost functions.
Abstract: The quality measures used in information retrieval are particularly difficult to optimize directly, since they depend on the model scores only through the sorted order of the documents returned for a given query Thus, the derivatives of the cost with respect to the model parameters are either zero, or are undefined In this paper, we propose a class of simple, flexible algorithms, called LambdaRank, which avoids these difficulties by working with implicit cost functions We describe LambdaRank using neural network models, although the idea applies to any differentiable function class We give necessary and sufficient conditions for the resulting implicit cost function to be convex, and we show that the general method has a simple mechanical interpretation We demonstrate significantly improved accuracy, over a state-of-the-art ranking algorithm, on several datasets We also show that LambdaRank provides a method for significantly speeding up the training phase of that ranking algorithm Although this paper is directed towards ranking, the proposed method can be extended to any non-smooth and multivariate cost functions

152 citations

01 Jan 2002
TL;DR: TREC-2001 saw the falling into abeyance of the Large Web Task but a strengthening and broadening of activities based on the 1.69 million page WTlOg corpus.
Abstract: TREC-2001 saw the falling into abeyance of the Large Web Task but a strengthening and broadening of activities based on the 1.69 million page WTlOg corpus. There were two tasks. The topic relevance task was like traditional TREC ad hoc but used queries taken from real web search logs from which description and narrative fields of a topic description were inferred by the topic developers. There were 50 topics. In the homepage finding task queries corresponded to the name of an entity whose home page (site entry page) was included in WTlOg. The challenge in this task was to return all of the homepages at the very top of the ranking. Cursory analysis suggests that once again, exploitation of link information did not help on the topic relevance task. By contrast, in the homepage finding task, the best performing run which did not make use of either link information or properties of the document's URL achieved only half of the mean reciprocal rank of the best run.

151 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