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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
26 Jun 2016
TL;DR: This paper presents the preliminary design of CompassQL, which defines a partial specification that describes enumeration constraints, and methods for choosing, ranking, and grouping recommended visualizations in a specification language for querying over the space of visualizations.
Abstract: Creating effective visualizations requires domain familiarity as well as design and analysis expertise, and may impose a tedious specification process. To address these difficulties, many visualization tools complement manual specification with recommendations. However, designing interfaces, ranking metrics, and scalable recommender systems remain important research challenges. In this paper, we propose a common framework for facilitating the development of visualization recommender systems in the form of a specification language for querying over the space of visualizations. We present the preliminary design of CompassQL, which defines (1) a partial specification that describes enumeration constraints, and (2) methods for choosing, ranking, and grouping recommended visualizations. To demonstrate the expressivity of the language, we describe existing recommender systems in terms of CompassQL queries. Finally, we discuss the prospective benefits of a common language for future visualization recommender systems.

103 citations

Journal ArticleDOI
TL;DR: A class of vector filters is developed, which are efficient smoothers in additive noise and can be designed to have detail-preserving characteristics and are used to develop ranked-order type estimators for multivariate image fields.
Abstract: The extension of ranking a set of elements in R to ranking a set of vectors in a p'th dimensional space R/sup p/ is considered. In the approach presented here vector ranking reduces to ordering vectors according to a sorted list of vector distances. A statistical analysis of this vector ranking is presented, and these vector ranking concepts are then used to develop ranked-order type estimators for multivariate image fields. A class of vector filters is developed, which are efficient smoothers in additive noise and can be designed to have detail-preserving characteristics. A statistical analysis is developed for the class of filters and a number of simulations were performed in order to quantitatively evaluate their performance. These simulations involve the estimation of both stationary multivariate random signals and color images in additive noise. >

103 citations

Patent
17 Dec 2001
TL;DR: In this paper, the authors present a dynamic search process that includes at least one ordered sequence of searches, based on text provided by a user's query, and the results of a first search are evaluated to determine how to formulate a second or subsequent search, whether to perform a second search, or whether or how to present to the user results from the search or searches performed up to that point.
Abstract: This document discusses, among other things, systems and methods for searching for relevant documents in a document corpus. Using, among other things, text provided by a user's query, the system undertakes a dynamic search process that includes at least one ordered sequence of searches. The results of a first search are evaluated to determine how to formulate a second or subsequent search, whether to perform a second or subsequent search, or whether or how to present to the user results from the search or searches performed up to that point. In one example, the first search uses tight criteria in conjunction with the language of the user's query. If the number of documents in the search results do not meet or exceed a threshold value, the search criteria is progressively loosened over subsequent searches. The search list may depend on, among other things, a characteristic of the user query or upon a result returned by a previous search on the user query.

103 citations

Proceedings ArticleDOI
01 Jun 2014
TL;DR: The UvA-ILLC submission of the BEER metric to WMT 14 metrics task is presented, with novel contributions of efficient tuning of a large number of features for maximizing correlation with human system ranking and novel features that give smoother sentence level scores.
Abstract: We present the UvA-ILLC submission of the BEER metric to WMT 14 metrics task. BEER is a sentence level metric that can incorporate a large number of features combined in a linear model. Novel contributions are (1) efficient tuning of a large number of features for maximizing correlation with human system ranking, and (2) novel features that give smoother sentence level scores.

103 citations

Posted Content
Canjia Li, Andrew Yates1, Sean MacAvaney, Ben He, Yingfei Sun 
TL;DR: An end-to-end Transformer-based model that considers document-level context for document reranking and leverages passage-level relevance representations to predict a document relevance score, overcoming the limitations of previous approaches.
Abstract: We present PARADE, an end-to-end Transformer-based model that considers document-level context for document reranking. PARADE leverages passage-level relevance representations to predict a document relevance score, overcoming the limitations of previous approaches that perform inference on passages independently. Experiments on two ad-hoc retrieval benchmarks demonstrate PARADE's effectiveness over such methods. We conduct extensive analyses on PARADE's efficiency, highlighting several strategies for improving it. When combined with knowledge distillation, a PARADE model with 72\% fewer parameters achieves effectiveness competitive with previous approaches using BERT-Base. Our code is available at \url{this https URL}.

103 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