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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
06 Aug 2006
TL;DR: This work considers a number of information retrieval metrics from the literature, including the rank of the first relevant result, the %no metric that penalizes a system only for retrieving no relevant results near the top, and the diversity of retrieved results when queries have multiple interpretations.
Abstract: Traditionally, information retrieval systems aim to maximize the number of relevant documents returned to a user within some window of the top. For that goal, the probability ranking principle, which ranks documents in decreasing order of probability of relevance, is provably optimal. However, there are many scenarios in which that ranking does not optimize for the users information need. One example is when the user would be satisfied with some limited number of relevant documents, rather than needing all relevant documents. We show that in such a scenario, an attempt to return many relevant documents can actually reduce the chances of finding any relevant documents. We consider a number of information retrieval metrics from the literature, including the rank of the first relevant result, the %no metric that penalizes a system only for retrieving no relevant results near the top, and the diversity of retrieved results when queries have multiple interpretations. We observe that given a probabilistic model of relevance, it is appropriate to rank so as to directly optimize these metrics in expectation. While doing so may be computationally intractable, we show that a simple greedy optimization algorithm that approximately optimizes the given objectives produces rankings for TREC queries that outperform the standard approach based on the probability ranking principle.

371 citations

Proceedings Article
21 Jun 2010
TL;DR: A general metric learning algorithm is presented, based on the structural SVM framework, to learn a metric such that rankings of data induced by distance from a query can be optimized against various ranking measures, such as AUC, Precision-at-k, MRR, MAP or NDCG.
Abstract: We study metric learning as a problem of information retrieval. We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that rankings of data induced by distance from a query can be optimized against various ranking measures, such as AUC, Precision-at-k, MRR, MAP or NDCG. We demonstrate experimental results on standard classification data sets, and a large-scale online dating recommendation problem.

371 citations

Journal ArticleDOI
TL;DR: The strategies that have evolved to deal with the problem of matching material and process attributes to design requirements are reviewed, the progress that has been made and the challenges that remain.

371 citations

Proceedings ArticleDOI
06 Jun 2018
TL;DR: The authors conceptualized extractive summarization as a sentence ranking task and proposed a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective, which outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.
Abstract: Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. We use our algorithm to train a neural summarization model on the CNN and DailyMail datasets and demonstrate experimentally that it outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.

365 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