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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
10 Oct 2004
TL;DR: This work proposes using query- class dependent weights within a hierarchial mixture-of-expert framework to combine multiple retrieval results with query-class associated weights, which can be learned from the development data efficiently and generalized to the unseen queries easily.
Abstract: Combining retrieval results from multiple modalities plays a crucial role for video retrieval systems, especially for automatic video retrieval systems without any user feedback and query expansion. However, most of current systems only utilize query independent combination or rely on explicit user weighting. In this work, we propose using query-class dependent weights within a hierarchial mixture-of-expert framework to combine multiple retrieval results. We first classify each user query into one of the four predefined categories and then aggregate the retrieval results with query-class associated weights, which can be learned from the development data efficiently and generalized to the unseen queries easily. Our experimental results demonstrate that the performance with query-class dependent weights can considerably surpass that with the query independent weights.

150 citations

Proceedings ArticleDOI
26 Oct 2008
TL;DR: In this poster, 22 pre-retrieval predictors are categorized and assessed on three different TREC test collections and such predictors base their predictions solely on query terms, the collection statistics and possibly external sources such as WordNet.
Abstract: The focus of research on query performance prediction is to predict the effectiveness of a query given a search system and a collection of documents. If the performance of queries can be estimated in advance of, or during the retrieval stage, specific measures can be taken to improve the overall performance of the system. In particular, pre-retrieval predictors predict the query performance before the retrieval step and are thus independent of the ranked list of results; such predictors base their predictions solely on query terms, the collection statistics and possibly external sources such as WordNet. In this poster, 22 pre-retrieval predictors are categorized and assessed on three different TREC test collections.

149 citations

Patent
Deepa Joshi1, John Thrall
20 Dec 2006
TL;DR: In this article, a system is described for discovering query intent based on search queries and concept networks, and the system may construct frequency vectors from log data corresponding to a submitted query and at least one related query submitted to one or more search engines.
Abstract: A system is described for discovering query intent based on search queries and concept networks. The system may construct frequency vectors from log data corresponding to a submitted query and at least one related query submitted to one or more search engines. The system may also construct a query intent vector based on the frequency vectors. The query intent vector may include frequency scores that represent the intent of the query.

149 citations

Proceedings ArticleDOI
25 Sep 2005
TL;DR: An automated technique for feature location: helping developers map features to relevant source code based on execution-trace analysis that is less sensitive with respect to the quality of the input and more effective when used by developers unfamiliar with the target system is introduced.
Abstract: This paper introduces an automated technique for feature location: helping developers map features to relevant source code. Like several other automated feature location techniques, ours is based on execution-trace analysis. We hypothesize that these techniques, which rely on making binary judgments about a code element's relevance to a feature, are overly sensitive to the quality of the input. The main contribution of this paper is to provide a more robust alternative, whose most distinguishing characteristic is that it employs ranking heuristics to determine a code element's relevance to a feature. We believe that our technique is less sensitive with respect to the quality of the input and we claim that it is more effective when used by developers unfamiliar with the target system. We validate our claim by applying our technique to three systems with comprehensive test suites. A developer unfamiliar with the target system spent a limited amount of effort preparing the test suite for analysis. Our results show that under these circumstances our ranking-based technique compares favorably to a technique based on binary judgements.

149 citations

Proceedings ArticleDOI
09 Feb 2011
TL;DR: This paper presents the quality-biased ranking method that promotes documents containing high-quality content, and penalizes low-quality documents, and consistently improves the retrieval performance of text-based and link-based retrieval methods that do not take into account the quality of the document content.
Abstract: Many existing retrieval approaches do not take into account the content quality of the retrieved documents, although link-based measures such as PageRank are commonly used as a form of document prior. In this paper, we present the quality-biased ranking method that promotes documents containing high-quality content, and penalizes low-quality documents. The quality of the document content can be determined by its readability, layout and ease-of-navigation, among other factors. Accordingly, instead of using a single estimate for document quality, we consider multiple content-based features that are directly integrated into a state-of- the-art retrieval method. These content-based features are easy to compute, store and retrieve, even for large web collections. We use several query sets and web collections to empirically evaluate the performance of our quality-biased retrieval method. In each case, our method consistently improves by a large margin the retrieval performance of text-based and link-based retrieval methods that do not take into account the quality of the document content.

149 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