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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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Journal ArticleDOI
TL;DR: It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents, and a novel application of P-R curves and average precision computations based on separate recall bases for documents of different degrees of relevance is proposed.
Abstract: This article proposes evaluation methods based on the use of nondichotomous relevance judgements in IR experiments. It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents. This is desirable from the user point of view in modern large IR environments. The proposed methods are (1) a novel application of P-R curves and average precision computations based on separate recall bases for documents of different degrees of relevance, and (2) generalized recall and precision based directly on multiple grade relevance assessments (i.e., not dichotomizing the assessments). We demonstrate the use of the traditional and the novel evaluation measures in a case study on the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance. The test was run with a best match retrieval system (InQuery1) in a text database consisting of newspaper articles. To gain insight into the retrieval process, one should use both graded relevance assessments and effectiveness measures that enable one to observe the differences, if any, between retrieval methods in retrieving documents of different levels of relevance. In modern times of information overload, one should pay attention, in particular, to the capability of retrieval methods retrieving highly relevant documents.

239 citations

Journal ArticleDOI
TL;DR: A deep look into the neural ranking models from different dimensions is taken to analyze their underlying assumptions, major design principles, and learning strategies to obtain a comprehensive empirical understanding of the existing techniques.
Abstract: Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions.

239 citations

Patent
11 Apr 2006
TL;DR: A search system for searching for electronic documents, and providing a search result in response to a search query is provided in this paper, which includes a search engine that executes a search based on the search query term and the equivalent terms.
Abstract: A search system for searching for electronic documents, and providing a search result in response to a search query is provided. The search system includes a processor, a user interface module adapted to receive a search query from a user, the search query having at least one search query term, and a query processing module that analyzes the search query term to identify candidate synonym words. The query processing module also determines which of the candidate synonym words are equivalent terms to the search query term, and in a same sense as the search query term. In addition, the search system includes a search engine that executes a search based on the search query term and the equivalent terms.

238 citations

Proceedings ArticleDOI
01 Sep 2001
TL;DR: The initial results of a new evaluation methodology which replaces human relevance judgments with a randomly selected mapping of documents to topics are proposed, which are referred to aspseudo-relevance judgments.
Abstract: The most prevalent experimental methodology for comparing the effectiveness of information retrieval systems requires a test collection, composed of a set of documents, a set of query topics, and a set of relevance judgments indicating which documents are relevant to which topics. It is well known that relevance judgments are not infallible, but recent retrospective investigation into results from the Text REtrieval Conference (TREC) has shown that differences in human judgments of relevance do not affect the relative measured performance of retrieval systems. Based on this result, we propose and describe the initial results of a new evaluation methodology which replaces human relevance judgments with a randomly selected mapping of documents to topics which we refer to aspseudo-relevance judgments.Rankings of systems with our methodology correlate positively with official TREC rankings, although the performance of the top systems is not predicted well. The correlations are stable over a variety of pool depths and sampling techniques. With improvements, such a methodology could be useful in evaluating systems such as World-Wide Web search engines, where the set of documents changes too often to make traditional collection construction techniques practical.

238 citations

Patent
Michael E. Barrett1, Alan Levin1
15 Jan 2002
TL;DR: In this article, a method of organizing information in which the search activities of previous users is monitored and such activity is used to organize information for future users is presented, where user activities are monitored from a time and use based perspective to insure more relevant results can be provided in response to a user's search for information.
Abstract: A method of organizing information in which the search activities of previous users is monitored and such activity is used to organize information for future users. The user activities are monitored from a time and use based perspective to insure more relevant results can be provided in response to a user's search for information.

238 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