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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
Jun Xu1, Tie-Yan Liu1, Min Lu2, Hang Li1, Wei-Ying Ma1 
20 Jul 2008
TL;DR: Experimental results show that the methods based on direct optimization of evaluation measures can always outperform conventional methods of Ranking SVM and RankBoost, however, no significant difference exists among the performances of the direct optimization methods themselves.
Abstract: One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures used in information retrieval such as Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG). Several such algorithms including SVMmap and AdaRank have been proposed and their effectiveness has been verified. However, the relationships between the algorithms are not clear, and furthermore no comparisons have been conducted between them. In this paper, we conduct a study on the approach of directly optimizing evaluation measures in learning to rank for Information Retrieval (IR). We focus on the methods that minimize loss functions upper bounding the basic loss function defined on the IR measures. We first provide a general framework for the study and analyze the existing algorithms of SVMmap and AdaRank within the framework. The framework is based on upper bound analysis and two types of upper bounds are discussed. Moreover, we show that we can derive new algorithms on the basis of this analysis and create one example algorithm called PermuRank. We have also conducted comparisons between SVMmap, AdaRank, PermuRank, and conventional methods of Ranking SVM and RankBoost, using benchmark datasets. Experimental results show that the methods based on direct optimization of evaluation measures can always outperform conventional methods of Ranking SVM and RankBoost. However, no significant difference exists among the performances of the direct optimization methods themselves.

159 citations

Patent
18 Jan 2006
TL;DR: In this paper, a system is disclosed for generating a search result list in response to a search request from a searcher using a computer network. But, the system is restricted to a set of documents having general web content.
Abstract: A system is disclosed for generating a search result list in response to a search request from a searcher using a computer network. A first database is maintained that includes a first plurality of search listings. A second database is maintained that includes documents having general web content. A search request is received from the searcher. A first set of search listings is identified from the first database having documents generating a match with the search request and a second set of search listings is identified from the second database having documents generating a match with the search request. A confidence score is determined for each listing from the first set of search listings wherein the confidence score is determined in accordance with a relevance of each listing when compared to the listings of the second set of search listings. The identified search listings from the first set of search listing are ordered in accordance, at least in part, with the confidence score for each search listing.

159 citations

Book ChapterDOI
05 Nov 2006
TL;DR: AKTiveRank is presented, a prototype system for ranking ontologies based on a number of structural metrics, which addresses the need for methods to evaluate and rank existing ontologies in terms of their relevance to the needs of the knowledge engineer.
Abstract: Ontology search and reuse is becoming increasingly important as the quest for methods to reduce the cost of constructing such knowledge structures continues A number of ontology libraries and search engines are coming to existence to facilitate locating and retrieving potentially relevant ontologies The number of ontologies available for reuse is steadily growing, and so is the need for methods to evaluate and rank existing ontologies in terms of their relevance to the needs of the knowledge engineer This paper presents AKTiveRank, a prototype system for ranking ontologies based on a number of structural metrics

159 citations

Proceedings ArticleDOI
Kun He, Yan Lu1, Stan Sclaroff
01 Jun 2018
TL;DR: This paper improves the learning of local feature descriptors by optimizing the performance of descriptor matching, which is a common stage that follows descriptor extraction in local feature based pipelines, and can be formulated as nearest neighbor retrieval.
Abstract: Extraction of local feature descriptors is a vital stage in the solution pipelines for numerous computer vision tasks. Learning-based approaches improve performance in certain tasks, but still cannot replace handcrafted features in general. In this paper, we improve the learning of local feature descriptors by optimizing the performance of descriptor matching, which is a common stage that follows descriptor extraction in local feature based pipelines, and can be formulated as nearest neighbor retrieval. Specifically, we directly optimize a ranking-based retrieval performance metric, Average Precision, using deep neural networks. This general-purpose solution can also be viewed as a listwise learning to rank approach, which is advantageous compared to recent local ranking approaches. On standard benchmarks, descriptors learned with our formulation achieve state-of-the-art results in patch verification, patch retrieval, and image matching,

158 citations

Proceedings ArticleDOI
18 Jun 2018
TL;DR: This paper investigates the use of natural language processing and information retrieval techniques to carry out natural language search directly over source code, i.e. without having a curated Q&A forum such as Stack Overflow at hand.
Abstract: Searching over large code corpora can be a powerful productivity tool for both beginner and experienced developers because it helps them quickly find examples of code related to their intent. Code search becomes even more attractive if developers could express their intent in natural language, similar to the interaction that Stack Overflow supports. In this paper, we investigate the use of natural language processing and information retrieval techniques to carry out natural language search directly over source code, i.e. without having a curated Q&A forum such as Stack Overflow at hand. Our experiments using a benchmark suite derived from Stack Overflow and GitHub repositories show promising results. We find that while a basic word–embedding based search procedure works acceptably, better results can be obtained by adding a layer of supervision, as well as by a customized ranking strategy.

158 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