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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
01 Jan 2011
TL;DR: This paper investigates how to rank the trajectory patterns mined from the uploaded photos with geotags and timestamps to reveal the collective wisdom recorded in the seemingly isolated photos and the individual travel sequences reflected by the geo-tagged photos.
Abstract: Social media such as those residing in the popular photo sharing websites is attracting increasing attention in recent years. As a type of user-generated data, wisdom of the crowd is embedded inside such social media. In particular, millions of users upload to Flickr their photos, many associated with temporal and geographical information. In this paper, we investigate how to rank the trajectory patterns mined from the uploaded photos with geotags and timestamps. The main objective is to reveal the collective wisdom recorded in the seemingly isolated photos and the individual travel sequences reflected by the geo-tagged photos. Instead of focusing on mining frequent trajectory patterns from geo-tagged social media, we put more effort into ranking the mined trajectory patterns and diversifying the ranking results. Through leveraging the relationships among users, locations and trajectories, we rank the trajectory patterns. We then use an exemplar-based algorithm to diversify the results in order to discover the representative trajectory patterns. We have evaluated the proposed framework on 12 different cities using a Flickr dataset and demonstrated its effectiveness.

107 citations

Journal ArticleDOI
TL;DR: This work describes the framework, explains the challenges, and evaluates the gain over a baseline machine learning approach, showing how this design can be used to implement solutions to particular challenges that arise in applying machine learning for evidence-based hypothesis evaluation.
Abstract: The final stage in the IBM DeepQA pipeline involves ranking all candidate answers according to their evidence scores and judging the likelihood that each candidate answer is correct. In DeepQA, this is done using a machine learning framework that is phase-based, providing capabilities for manipulating the data and applying machine learning in successive applications. We show how this design can be used to implement solutions to particular challenges that arise in applying machine learning for evidence-based hypothesis evaluation. Our approach facilitates an agile development environment for DeepQA; evidence scoring strategies can be easily introduced, revised, and reconfigured without the need for error-prone manual effort to determine how to combine the various evidence scores. We describe the framework, explain the challenges, and evaluate the gain over a baseline machine learning approach.

107 citations

Journal ArticleDOI
TL;DR: By taking advantage of this ranking-based switching mechanism, a class of new fuzzy multi-instant observers are achieved and more relaxed design conditions can be obtained for ensuring the asymptotically stability of the developed state estimation error system.
Abstract: This paper generalizes recent results on multi-instant observer design for discrete-time Takagi–Sugeno fuzzy systems through a valid ranking-based switching approach. The approach hereby develops a concentrated subdivision of spanning space composed of normalized fuzzy weighting functions and then substantially produces a new ranking-based switching mechanism. By taking advantage of this ranking-based switching mechanism, a class of new fuzzy multi-instant observers are achieved and more relaxed design conditions with respect to the recent work can be obtained for ensuring the asymptotically stability of the developed state estimation error system. Two illustrative examples are provided to validate the effectiveness of the result given in this study.

107 citations

Journal ArticleDOI
TL;DR: A revised method is proposed which can avoid problems with the Wang's method for ranking fuzzy numbers and is easy to rank fuzzy numbers in a way similar to the original method.
Abstract: Recently, Wang, Liu, Fan, and Feng (in press) proposed an approach to overcome the limitations of the existing studies and simplify the computational procedures based on the LR deviation degree of fuzzy number. However, there were some problems with the ranking method. In this paper, we want to indicate these problems of Wang's method and then propose a revised method which can avoid these problems for ranking fuzzy numbers. Since the revised method is based on the Wang's method, it is easy to rank fuzzy numbers in a way similar to the original method. The method is illustrated by numerical examples and compared with other methods.

106 citations

Journal ArticleDOI
TL;DR: In this paper, a case study of a hydro-ecological management problem is analyzed by means of multi criterion decision-making (MCDM) techniques, including preference ranking organization (PROMETHEE), geometrical analysis for interactive assistance (GAIA), multi criterion Q-analysis (MCQA-I, II, III), compromise programming (CP), and cooperative game theory (CGT).

106 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