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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
05 Jul 2008
TL;DR: This work formulate the learning problem of predicting diverse subsets and derive a training method based on structural SVMs that explicitly trains to diversify results.
Abstract: In many retrieval tasks, one important goal involves retrieving a diverse set of results (e.g., documents covering a wide range of topics for a search query). First of all, this reduces redundancy, effectively showing more information with the presented results. Secondly, queries are often ambiguous at some level. For example, the query "Jaguar" can refer to many different topics (such as the car or feline). A set of documents with high topic diversity ensures that fewer users abandon the query because no results are relevant to them. Unlike existing approaches to learning retrieval functions, we present a method that explicitly trains to diversify results. In particular, we formulate the learning problem of predicting diverse subsets and derive a training method based on structural SVMs.

178 citations

Proceedings ArticleDOI
07 Dec 2015
TL;DR: DeepBox as mentioned in this paper uses convolutional neural networks (CNNs) to rerank proposals from a bottom-up method, which leads to a 4.5-point gain in detection mAP.
Abstract: Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that "objectness" is in fact a high level construct. We argue for a data-driven, semantic approach for ranking object proposals. Our framework, which we call DeepBox, uses convolutional neural networks (CNNs) to rerank proposals from a bottom-up method. We use a novel four-layer CNN architecture that is as good as much larger networks on the task of evaluating objectness while being much faster. We show that DeepBox significantly improves over the bottom-up ranking, achieving the same recall with 500 proposals as achieved by bottom-up methods with 2000. This improvement generalizes to categories the CNN has never seen before and leads to a 4.5-point gain in detection mAP. Our implementation achieves this performance while running at 260 ms per image.

178 citations

Patent
10 Jul 2000
TL;DR: In this article, an automated method of creating or updating a database of resumes and related documents is proposed. But, the method is limited to the retrieval of documents from a network of documents, where the document is the most relevant document to the subject taxonomy stored in the retrieval priority list.
Abstract: An automated method of creating or updating a database of resumes and related documents, the method comprising, a) entering at least one example document that is relevant to a subject taxonomy in a retrieval priority list, if there is a plurality of example documents stored in the retrieval priority list, ranking the example documents according to the relevancy of the example documents to the subject taxonomy; b) retrieving a document from a network of documents, where the document is the most relevant document to the subject taxonomy stored in the retrieval priority list; c) harvesting information from specified fields of the document; d) classifying the information into one or more classes according to specified categories of the subject taxonomy; e) storing the information into a database; f) determining whether the information are links to other documents; g) ranking the link's according to relevancy to the subject taxonomy, and storing the links in the retrieval priority list according to the relevancy; h) terminating the method, provided the method's stop criteria have been met; and i) repeating steps b) through h), provided the method's stop criteria has not been met.

178 citations

Posted Content
Ledell Wu, Adam Fisch1, Sumit Chopra1, Keith Adams1, Antoine Bordes1, Jason Weston1 
TL;DR: Empirical results on a number of tasks show that StarSpace is highly competitive with existing methods, whilst also being generally applicable to new cases where those methods are not.
Abstract: We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and learning word, sentence or document level embeddings. In each case the model works by embedding those entities comprised of discrete features and comparing them against each other -- learning similarities dependent on the task. Empirical results on a number of tasks show that StarSpace is highly competitive with existing methods, whilst also being generally applicable to new cases where those methods are not.

177 citations

Proceedings Article
01 Jun 2007
TL;DR: This paper presents an application of PageRank, a random-walk model originally devised for ranking Web search results, to ranking WordNet synsets in terms of how strongly they possess a given semantic property.
Abstract: This paper presents an application of PageRank, a random-walk model originally devised for ranking Web search results, to ranking WordNet synsets in terms of how strongly they possess a given semantic property. The semantic properties we use for exemplifying the approach are positivity and negativity, two properties of central importance in sentiment analysis. The idea derives from the observation that WordNet may be seen as a graph in which synsets are connected through the binary relation “a term belonging to synset sk occurs in the gloss of synset si”, and on the hypothesis that this relation may be viewed as a transmitter of such semantic properties. The data for this relation can be obtained from eXtended WordNet, a publicly available sensedisambiguated version of WordNet. We argue that this relation is structurally akin to the relation between hyperlinked Web pages, and thus lends itself to PageRank analysis. We report experimental results supporting our intuitions.

176 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