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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
19 Jul 2009
TL;DR: Experimental results on three datasets show that Ranking SVM significantly outperforms the baseline methods of SVM and Naive Bayes, indicating that it is better to exploit learning to rank techniques in keyphrase extraction.
Abstract: This paper addresses the issue of automatically extracting keyphrases from a document. Previously, this problem was formalized as classification and learning methods for classification were utilized. This paper points out that it is more essential to cast the problem as ranking and employ a learning to rank method to perform the task. Specifically, it employs Ranking SVM, a state-of-art method of learning to rank, in keyphrase extraction. Experimental results on three datasets show that Ranking SVM significantly outperforms the baseline methods of SVM and Naive Bayes, indicating that it is better to exploit learning to rank techniques in keyphrase extraction.

148 citations

Proceedings ArticleDOI
24 Mar 2009
TL;DR: This paper presents Zerber+R -- a ranking model which allows for privacy-preserving top-k retrieval from an outsourced inverted index and proposes a relevance score transformation function which makes relevance scores of different terms indistinguishable, such that even if stored on an untrusted server they do not reveal information about the indexed data.
Abstract: Privacy-preserving document exchange among collaboration groups in an enterprise as well as across enterprises requires techniques for sharing and search of access-controlled information through largely untrusted servers. In these settings search systems need to provide confidentiality guarantees for shared information while offering IR properties comparable to the ordinary search engines. Top-k is a standard IR technique which enables fast query execution on very large indexes and makes systems highly scalable. However, indexing access-controlled information for top-k retrieval is a challenging task due to the sensitivity of the term statistics used for ranking.In this paper we present Zerber+R -- a ranking model which allows for privacy-preserving top-k retrieval from an outsourced inverted index. We propose a relevance score transformation function which makes relevance scores of different terms indistinguishable, such that even if stored on an untrusted server they do not reveal information about the indexed data. Experiments on two real-world data sets show that Zerber+R makes economical usage of bandwidth and offers retrieval properties comparable with an ordinary inverted index.

148 citations

Proceedings Article
15 Apr 2009
TL;DR: This paper shows how to naturally derive a space of possible ranking criteria, and shows that several recent contributions in the feature selection literature are points within this continuous space, and that there exist many points that have never been explored.
Abstract: Feature Filters are among the simplest and fastest approaches to feature selection. A filter defines a statistical criterion, used to rank features on how useful they are expected to be for classification. The highest ranking features are retained, and the lowest ranking can be discarded. A common approach is to use the Mutual Information between the feature and class label. This area has seen a recent flurry of activity, resulting in a confusing variety of heuristic criteria all based on mutual information, and a lack of a principled way to understand or relate them. The contribution of this paper is a unifying theoretical understanding of such filters. In contrast to current methods which manually construct filter criteria with particular properties, we show how to naturally derive a space of possible ranking criteria. We will show that several recent contributions in the feature selection literature are points within this continuous space, and that there exist many points that have never been explored.

148 citations

Book ChapterDOI
07 Dec 2009
TL;DR: The XER tasks and the evaluation procedure used at the XER track in 2009, where a new version of Wikipedia was used as underlying collection are described; and the approaches adopted by the participants are summarized.
Abstract: In some situations search engine users would prefer to retrieve entities instead of just documents. Example queries include "Italian Nobel prize winners", "Formula 1 drivers that won the Monaco Grand Prix", or "German spoken Swiss cantons". The XML Entity Ranking (XER) track at INEX creates a discussion forum aimed at standardizing evaluation procedures for entity retrieval. This paper describes the XER tasks and the evaluation procedure used at the XER track in 2009, where a new version of Wikipedia was used as underlying collection; and summarizes the approaches adopted by the participants.

147 citations

Patent
05 Aug 2011
TL;DR: In this article, a search query is evaluated to determine whether it is the type of query that a user might want to ask to a friend, and if the query is of such a type, then the search engine may examine a social graph to determine which friends of the user who entered the query may have information that is relevant to answering the query.
Abstract: Search results may include both objective results and person results. In one example, a search query is evaluated to determine whether it is the type of query that a user might want to ask to a friend. If the query is of such a type, then the search engine may examine a social graph to determine which friends of the user who entered the query may have information that is relevant to answering the query. If such friends exist, then the friends may be displayed along with objective search results, along with an explanation of each friend's relevance to the query. Clicking on a person in the results may cause a conversation to be initiated with that person, thereby allowing the user who entered the query to ask his or her friend about the subject of the query.

147 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