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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
20 May 1997
TL;DR: In this article, a method for optimizing the cost of searches through a multimedia repository is disclosed where the repository contains a plurality of objects having at least two different attributes such as color in a newspaper photograph and text in the subtitle.
Abstract: A method for optimizing the cost of searches through a multimedia repository is disclosed where the repository contains a plurality of objects having at least two different attributes such as color in a newspaper photograph and text in the subtitle. The method comprises selecting a ranking expression, translating the ranking expression into resulting filter conditions and then optimizing the resulting filter conditions to perform the search. A database look-up step is included which determines the cost of performing searches over the various subconditions of the filter condition. The least costly subcondition is searched first to retrieve objects from the multimedia repository. The remaining subconditions are then evaluated on the retrieved objects using either a search step or probe step depending upon the determined cost to perform each. A further database look-up step predicts a grade of match necessary in the translated ranking expression to retrieve at least the number of objects requested in the search.

216 citations

Journal ArticleDOI
TL;DR: The empirical results support the hypothesis that the stability of ranking information decreases with decreasing rank for a ranking of 4 alternatives, and indicate the best strategy to combine the ranks is to include rank-specific scale and other bias parameters.

216 citations

Proceedings ArticleDOI
18 May 2013
TL;DR: A recommender (called Refoqus) based on machine learning is proposed, which is trained with a sample of queries and relevant results and automatically recommends a reformulation strategy that should improve its performance, based on the properties of the query.
Abstract: There are more than twenty distinct software engineering tasks addressed with text retrieval (TR) techniques, such as, traceability link recovery, feature location, refactoring, reuse, etc. A common issue with all TR applications is that the results of the retrieval depend largely on the quality of the query. When a query performs poorly, it has to be reformulated and this is a difficult task for someone who had trouble writing a good query in the first place. We propose a recommender (called Refoqus) based on machine learning, which is trained with a sample of queries and relevant results. Then, for a given query, it automatically recommends a reformulation strategy that should improve its performance, based on the properties of the query. We evaluated Refoqus empirically against four baseline approaches that are used in natural language document retrieval. The data used for the evaluation corresponds to changes from five open source systems in Java and C++ and it is used in the context of TR-based concept location in source code. Refoqus outperformed the baselines and its recommendations lead to query performance improvement or preservation in 84% of the cases (in average).

215 citations

Journal ArticleDOI
TL;DR: The impossibility theorems that abound in the theory of social choice show that there can be no satisfactory method for electing and ranking in the context of the traditional, 700-year-old model, and a new theory is proposed that avoids all impossibilities with a simple and eminently practical method, “the majority judgement.”
Abstract: The impossibility theorems that abound in the theory of social choice show that there can be no satisfactory method for electing and ranking in the context of the traditional, 700-year-old model. A more realistic model, whose antecedents may be traced to Laplace and Galton, leads to a new theory that avoids all impossibilities with a simple and eminently practical method, “the majority judgement.” It has already been tested.

215 citations

Journal ArticleDOI
01 Jan 2003
TL;DR: An efficient peer-to-peer information retrieval system, pSearch; that supports state-of-the-art content- and semantic-based full-text searches, avoiding the scalability problem of existing systems that employ centralized indexing, or index/query flooding.
Abstract: We describe an efficient peer-to-peer information retrieval system, pSearch; that supports state-of-the-art content- and semantic-based full-text searches. pSearch avoids the scalability problem of existing systems that employ centralized indexing, or index/query flooding. It also avoids the nondeterminism that is exhibited by heuristic-based approaches. In pSearch; documents in the network are organized around their vector representations (based on modern document ranking algorithms) such that the search space for a given query is organized around related documents, achieving both efficiency and accuracy.

215 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