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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.


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Proceedings ArticleDOI
28 Jul 2013
TL;DR: This paper proposes two complementary evaluation measures -- Reliability and Sensitivity -- for the generic Document Organization task which are derived from a proposed set of formal constraints (properties that any suitable measure must satisfy).
Abstract: A number of key Information Access tasks -- Document Retrieval, Clustering, Filtering, and their combinations -- can be seen as instances of a generic {\em document organization} problem that establishes priority and relatedness relationships between documents (in other words, a problem of forming and ranking clusters). As far as we know, no analysis has been made yet on the evaluation of these tasks from a global perspective. In this paper we propose two complementary evaluation measures -- Reliability and Sensitivity -- for the generic Document Organization task which are derived from a proposed set of formal constraints (properties that any suitable measure must satisfy). In addition to be the first measures that can be applied to any mixture of ranking, clustering and filtering tasks, Reliability and Sensitivity satisfy more formal constraints than previously existing evaluation metrics for each of the subsumed tasks. Besides their formal properties, its most salient feature from an empirical point of view is their strictness: a high score according to the harmonic mean of Reliability and Sensitivity ensures a high score with any of the most popular evaluation metrics in all the Document Retrieval, Clustering and Filtering datasets used in our experiments.

112 citations

Journal ArticleDOI
TL;DR: In this article , the Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS) is used to rank the alternative solutions to these barriers and the overall ranking indicates that "technological complexity" ranks highest among all sub-barriers across all categories.

111 citations

Book ChapterDOI
01 Jan 2010
TL;DR: This introduction gives a brief overview of the field of preference learning and tries to establish a unified terminology, proposing a categorization of ranking problems into object ranking, instance ranking, and label ranking.
Abstract: This introduction gives a brief overview of the field of preference learning and, along the way, tries to establish a unified terminology. Special emphasis will be put on learning to rank, which is by now one of the most extensively studied problem tasks in preference learning and also prominently represented in this book. We propose a categorization of ranking problems into object ranking, instance ranking, and label ranking. Moreover, we introduce these scenarios in a formal way, discuss different ways in which the learning of ranking functions can be approached, and explain how the contributions collected in this book relate to this categorization. Finally, we also highlight some important applications of preference learning methods.

111 citations

Book ChapterDOI
Milad Shokouhi1
02 Apr 2007
TL;DR: A new collection-selection method based on the ranking of downloaded sample documents that can significantly outperform other state-of-the-art algorithms in most cases is proposed and tested on six testbeds.
Abstract: Collection selection is one of the key problems in distributed information retrieval. Due to resource constraints it is not usually feasible to search all collections in response to a query. Therefore, the central component (broker) selects a limited number of collections to be searched for the submitted queries. During the past decade, several collection selection algorithms have been introduced. However, their performance varies on different testbeds. We propose a new collection-selection method based on the ranking of downloaded sample documents. We test our method on six testbeds and show that our technique can significantly outperform other state-of-the-art algorithms in most cases. We also introduce a new testbed based on the TREC GOV2 documents.

111 citations

Proceedings ArticleDOI
24 Oct 2011
TL;DR: This paper attempts to predict the POI tag of a tweet based on its textual content and time of posting, and uses web pages retrieved by search engines as an additional source of evidence to tackle the sparsity of tweets tagged with POIs.
Abstract: Twitter is a widely-used social networking service which enables its users to post text-based messages, so-called tweets. POI tags on tweets can show more human-readable high-level information about a place rather than just a pair of coordinates. In this paper, we attempt to predict the POI tag of a tweet based on its textual content and time of posting. Potential applications include accurate positioning when GPS devices fail and disambiguating places located near each other. We consider this task as a ranking problem, i.e., we try to rank a set of candidate POIs according to a tweet by using language and time models. To tackle the sparsity of tweets tagged with POIs, we use web pages retrieved by search engines as an additional source of evidence. From our experiments, we find that users indeed leak some information about their accurate locations in their tweets.

111 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