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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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TL;DR: This article 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.

164 citations

Journal ArticleDOI
TL;DR: An effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions is introduced and a new two-phase personalized agglomerative clustering algorithm is proposed that is able to generate personalized query clusters.
Abstract: The exponential growth of information on the Web has introduced new challenges for building effective search engines. A major problem of Web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the Web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new two-phase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors' knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.

164 citations

Proceedings ArticleDOI
29 Sep 2014
TL;DR: Experimental results show that Multric localizes faults more effectively than state-of-art metrics, such as Tarantula, Ochiai, and Ample.
Abstract: Fault localization is an inevitable step in software debugging. Spectrum-based fault localization consists in computing a ranking metric on execution traces to identify faulty source code. Existing empirical studies on fault localization show that there is no optimal ranking metric for all faults in practice. In this paper, we propose Multric, a learning-based approach to combining multiple ranking metrics for effective fault localization. In Multric, a suspiciousness score of a program entity is a combination of existing ranking metrics. Multric consists two major phases: learning and ranking. Based on training faults, Multric builds a ranking model by learning from pairs of faulty and non-faulty source code elements. When a new fault appears, Multric computes the final ranking with the learned model. Experiments are conducted on 5386 seeded faults in ten open-source Java programs. We empirically compare Multric against four widely-studied metrics and three recently-proposed one. Our experimental results show that Multric localizes faults more effectively than state-of-art metrics, such as Tarantula, Ochiai, and Ample.

164 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: A novel global ranking algorithm, applied to a Markov network built upon the 3D map, which takes account of not only visual similarities between individual 2D-3D matches, but also their global compatibilities among all matching pairs found in the scene.
Abstract: Given an image of a street scene in a city, this paper develops a new method that can quickly and precisely pinpoint at which location (as well as viewing direction) the image was taken, against a pre-stored large-scale 3D point-cloud map of the city. We adopt the recently developed 2D-3D direct feature matching framework for this task [23,31,32,42–44]. This is a challenging task especially for large-scale problems. As the map size grows bigger, many 3D points in the wider geographical area can be visually very similar–or even identical–causing severe ambiguities in 2D-3D feature matching. The key is to quickly and unambiguously find the correct matches between a query image and the large 3D map. Existing methods solve this problem mainly via comparing individual features’ visual similarities in a local and per feature manner, thus only local solutions can be found, inadequate for large-scale applications. In this paper, we introduce a global method which harnesses global contextual information exhibited both within the query image and among all the 3D points in the map. This is achieved by a novel global ranking algorithm, applied to a Markov network built upon the 3D map, which takes account of not only visual similarities between individual 2D-3D matches, but also their global compatibilities (as measured by co-visibility) among all matching pairs found in the scene. Tests on standard benchmark datasets show that our method achieved both higher precision and comparable recall, compared with the state-of-the-art.

164 citations

Patent
13 Mar 2006
TL;DR: In this article, a web site for user suggestions of products, services or other information is proposed, where the Suggestor also submits tags with those suggestions. To the extent subsequent users use the same tags to access or purchase the user suggestion, the suggesting user will be rewarded.
Abstract: A web site for user suggestions of products, services or other information. The Suggestor also submits tags with those suggestions. To the extent subsequent users use the same tags to access or purchase the user suggestion, the suggesting user will be rewarded. The present invention also provides mechanisms for disbursing rewards for “finding-and-buying-thru-tags”, ranking suggestions, enabling various privacy preserving communications and deal validation mechanisms among shoppers, Suggestors and their social networks.

163 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