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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
19 Nov 2002
TL;DR: A search engine that utilizes both record-based data and user activity data to develop, update and refine ranking protocols and identify words and phrases that give rise to search ambiguity as discussed by the authors, so that the engine can interact with the user to better respond to user queries and enhance data acquisition from databases, intranets and internets.
Abstract: A search engine is disclosed that utilizes both record based data and user activity data to develop, update and refine ranking protocols and to identify words and phrases that give rise to search ambiguity so that the engine can interact with the user to better respond to user queries and enhance data acquisition from databases, intranets and internets.

172 citations

Posted Content
TL;DR: In this article, a critical analysis of the "Academic Ranking of World Universities", published every year by the Institute of Higher Education of the Jiao Tong University in Shanghai and more commonly known as the Shanghai ranking, is presented.
Abstract: This paper proposes a critical analysis of the "Academic Ranking of World Universities", published every year by the Institute of Higher Education of the Jiao Tong University in Shanghai and more commonly known as the Shanghai ranking. After having recalled how the ranking is built, we first discuss the relevance of the criteria and then analyze the proposed aggregation method. Our analysis uses tools and concepts from Multiple Criteria Decision Making (MCDM). Our main conclusions are that the criteria that are used are not relevant, that the aggregation methodology is plagued by a number of major problems and that the whole exercise suffers from an insufficient attention paid to fundamental structuring issues. Hence, our view is that the Shanghai ranking, in spite of the media coverage it receives, does not qualify as a useful and pertinent tool to discuss the "quality" of academic institutions, let alone to guide the choice of students and family or to promote reforms of higher education systems. We outline the type of work that should be undertaken to oer sound alternatives to the Shanghai ranking.

172 citations

Proceedings ArticleDOI
01 Sep 2013
TL;DR: This study shows that it is possible to propagate the query information along the unlabelled data manifold in an unsupervised way to obtain robust ranking results, and demonstrates that the performance of existing supervised metric learning methods can be significantly boosted once integrated into the proposed manifold ranking-based framework.
Abstract: Existing person re-identification methods conventionally rely on labelled pairwise data to learn a task-specific distance metric for ranking. The value of unlabelled gallery instances is generally overlooked. In this study, we show that it is possible to propagate the query information along the unlabelled data manifold in an unsupervised way to obtain robust ranking results. In addition, we demonstrate that the performance of existing supervised metric learning methods can be significantly boosted once integrated into the proposed manifold ranking-based framework. Extensive evaluation is conducted on three benchmark datasets.

172 citations

Proceedings ArticleDOI
28 Jul 2013
TL;DR: This article proposes a novel TF-IDF term weighting scheme that employs two different within document term frequency normalizations to capture two different aspects of term saliency.
Abstract: Term weighting schemes are central to the study of information retrieval systems. This article proposes a novel TF-IDF term weighting scheme that employs two different within document term frequency normalizations to capture two different aspects of term saliency. One component of the term frequency is effective for short queries, while the other performs better on long queries. The final weight is then measured by taking a weighted combination of these components, which is determined on the basis of the length of the corresponding query. Experiments conducted on a large number of TREC news and web collections demonstrate that the proposed scheme almost always outperforms five state of the art retrieval models with remarkable significance and consistency. The experimental results also show that the proposed model achieves significantly better precision than the existing models.

172 citations

Proceedings ArticleDOI
23 Oct 2008
TL;DR: This paper proposes Social Ranking, a method that exploits recommender system techniques to increase the efficiency of searches within Web 2.0, and proposes a mechanism to answer a user's query that ranks content based on the inferred semantic distance of the query to the tags associated to such content, weighted by the similarity of the querying user to the users who created those tags.
Abstract: Social (or folksonomic) tagging has become a very popular way to describe, categorise, search, discover and navigate content within Web 2.0 websites. Unlike taxonomies, which overimpose a hierarchical categorisation of content, folksonomies empower end users by enabling them to freely create and choose the categories (in this case, tags) that best describe some content. However, as tags are informally defined, continually changing, and ungoverned, social tagging has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of users and the noise they introduce. In this paper, we propose Social Ranking, a method that exploits recommender system techniques to increase the efficiency of searches within Web 2.0. We measure users' similarity based on their past tag activity. We infer tags' relationships based on their association to content. We then propose a mechanism to answer a user's query that ranks (recommends) content based on the inferred semantic distance of the query to the tags associated to such content, weighted by the similarity of the querying user to the users who created those tags. A thorough evaluation conducted on the CiteULike dataset demonstrates that Social Ranking neatly improves coverage, while not compromising on accuracy.

171 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