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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
01 Nov 2011
TL;DR: This paper takes advantage of single-valued functions that evaluate rankings to develop a family of feature selection methods based on the genetic algorithm approach, tailored to improve the accuracy of content-based image retrieval systems.
Abstract: In this paper, we take advantage of single-valued functions that evaluate rankings to develop a family of feature selection methods based on the genetic algorithm approach, tailored to improve the accuracy of content-based image retrieval systems. Experiments on three image datasets, comprising images of breast and lung nodules, showed that developing functions to evaluate the ranking quality allows improving retrieval performance. This approach produces significantly better results than those of other fitness function approaches, such as the traditional wrapper and than filter feature selection algorithms.

99 citations

01 Jun 2015
TL;DR: The authors proposed a novel approach where the output layer of a character-level RNN language model is split into several independent predictive sub-models, each representing an author, while the recurrent layer is shared by all.
Abstract: Recurrent neural networks (RNNs) are very good at modelling the flow of text, but typically need to be trained on a far larger corpus than is available for the PAN 2015 Author Identification task. This paper describes a novel approach where the output layer of a character-level RNN language model is split into several independent predictive sub-models, each representing an author, while the recurrent layer is shared by all. This allows the recurrent layer to model the language as a whole without over-fitting, while the outputs select aspects of the underlying model that reflect their author's style. The method proves competitive, ranking first in two of the four languages.

99 citations

Patent
07 Sep 2005
TL;DR: In this article, a content-addressable and searchable storage system for managing and exploring massive amounts of feature-rich data such as images, audio or scientific data is presented, which comprises a segmentation and feature extraction unit for segmenting data corresponding to an object into a plurality of data segments and generating a feature vector for each data segment.
Abstract: A content-addressable and searchable storage system for managing and exploring massive amounts of feature-rich data such as images, audio or scientific data, is shown. The system comprises a segmentation and feature extraction unit for segmenting data corresponding to an object into a plurality of data segments and generating a feature vector for each data segment; a sketch construction component for converting a feature vector into a compact bit-vector corresponding to the object; a similarity index comprising a plurality of compact bit-vectors corresponding to a plurality of objects; and an index insertion component for inserting a compact bit-vector corresponding to an object into the similarity index. The system may further comprise an indexing unit for identifying a candidate set of objects from said similarity index based upon a compact bit-vector corresponding to a query object. Still further, the system may additionally comprise a similarity ranking component for ranking objects in said candidate set by estimating their distances to the query object.

99 citations

Journal ArticleDOI
01 Sep 1988
TL;DR: A number of algorithmic tools that have been found useful in the construction of parallel algorithms are described; among these are prefix computation, ranking, Euler tours, ear decomposition, and matrix calculations.
Abstract: We have described a number of algorithmic tools that have been found useful in the construction of parallel algorithms; among these are prefix computation, ranking, Euler tours, ear decomposition, and matrix calculations. We have also described some of the applications of these tools, and listed many other applications. These algorithms seem likely to be useful not only in their own right, but also as examples of ways to break up other problems into parts suitable for parallel solution.

99 citations

Proceedings ArticleDOI
28 Mar 2011
TL;DR: This work proposes a method to extend the reach of query assistance techniques (and in particular query recommendation) to long-tail queries by reasoning about rules between query templates rather than individual query transitions, as currently done in query-flow graph models.
Abstract: The ability to aggregate huge volumes of queries over a large population of users allows search engines to build precise models for a variety of query-assistance features such as query recommendation, correction, etc. Yet, no matter how much data is aggregated, the long-tail distribution implies that a large fraction of queries are rare. As a result, most query assistance services perform poorly or are not even triggered on long-tail queries. We propose a method to extend the reach of query assistance techniques (and in particular query recommendation) to long-tail queries by reasoning about rules between query templates rather than individual query transitions, as currently done in query-flow graph models. As a simple example, if we recognize that 'Montezuma' is a city in the rare query "Montezuma surf" and if the rule 'city surf → beach has been observed, we are able to offer "Montezuma beach" as a recommendation, even if the two queries were never observed in a same session. We conducted experiments to validate our hypothesis, first via traditional small-scale editorial assessments but more interestingly via a novel automated large scale evaluation methodology. Our experiments show that general coverage can be relatively increased by 24% using templates without penalizing quality. Furthermore, for 36% of the 95M queries in our query flow graph, which have no out edges and thus could not be served recommendations, we can now offer at least one recommendation in 98% of the cases.

99 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