Various methods, known as Multiple Criteria Decision Making Methods (MCDM), have been proposed to assist decision makers in the process of ranking alternatives. Given the variability of available methods, choosing an MCDM ranking method is a difficult task. There are key factors in the process of choosing an MCDM method such as: (i) available time; (ii) effort required by a given approach; (iii...

Describes a method to provide an independent, community-sourced set of best practice criteria with which assess global university rankings and identify the extent sample six rankings, Academic Ranking World Universities (ARWU), CWTS Leiden, QS University Rankings (QS WUR), Times Higher Education (THE U-Multirank, US News & Report Best Global Universities, met those criteria. The fell into f...

We study personalized web ranking algorithms based on the existence of document clusterings. Motivated by the topic sensitive page ranking of Haveliwala [20], we develop and implement an efficient “local-cluster” algorithm by extending the web search algorithm of Achlioptas, Fiat, Karlin and McSherry [10]. We propose some formal criteria for evaluating such personalized ranking algorithms and p...

Analytic hierarchy process (AHP) is a frequently used method for ranking alternatives. The alternatives to be ranked are modeled based on a set of criteria. The ranking computed by AHP is trusted on faith notwithstanding the fact that a model is an approximation and liable to failure. We present a model to estimate the trust in the AHP ranking. The model helps in determining if it is prudent to...

This paper considers the problem of learning the ranking of a set of alternatives based upon incomplete information (e.g., a limited number of observations). We describe two algorithms for hypothesis ranking and their application for probably approximately correct (PAC) and expected loss (EL) learning criteria. Empirical results are provided to demonstrate the effectiveness of these ranking pro...

We study personalized web ranking algorithms based on the existence of document clusterings. Motivated by the topic sensitive page ranking of Haveliwala [20], we develop and implement an efficient “local-cluster” algorithm by extending the web search algorithm of Achlioptas et al. [10]. We propose some formal criteria for evaluating such personalized ranking algorithms and provide some prelimin...

To fight the problem of information overload in huge information sources like large document repositories, e. g. citeseer, or internet websites you need a selection criteria: some kind of ranking is required. Ranking methods like PageRank use the document reference network structure. However, these rankings do not distinguish different reference semantics. We enhance these rankings by incorpora...

In order to survive in today's competitive world, organizations are looking to increase their efficiency and effectiveness. Therefore, they should focus on inputs that are more important than the rest of the inputs in their success. This paper aimed at ranking 7Ms (Management, Man power, Marketing, Material, Machinery, Methods and Money) and their sub-criteria of production in Iran. By improvin...

We study personalized web ranking algorithms based on the existence of document clusterings. Motivated by the topic sensitive page ranking of Haveliwala [20], we develop and implement an efficient “local-cluster” algorithm by extending the web search algorithm of Achlioptas et al. [10]. We propose some formal criteria for evaluating such personalized ranking algorithms and provide some prelimin...

Delphi as a consensus development technique enables anonymous, systematic refinement of expert opinion with the aim of arriving at a combined or consensual position. In this study, we determined the criteria and their weights for Iranian Medical Schools' ranking through a Delphi process. An expert committee devised 13 proposed criteria with 32 indicators with their weights, which were arranged ...