Route instructions for pedestrians are usually better understood if they include references to landmarks, andmoreover, these landmarks should be as salient as possible. In this paper, we present an approach for automatically deriving a mathematical model of salience directly from route instructions given by humans. Each possible landmark that a person can refer to in a given situation is modell...

The assumptions underlying the Probability Ranking Principle (PRP) have led to a number of alternative approaches that cater or compensate for the PRP’s limitations. All alternatives deviate from the PRP by incorporating dependencies. This results in a re-ranking that promotes or demotes documents depending upon their relationship with the documents that have been already ranked. In this paper,...

This paper introduces two new methods for label ranking based on a probabilistic model of ranking data, called the Plackett-Luce model. The idea of the first method is to use the PL model to fit locally constant probability models in the context of instance-based learning. As opposed to this, the second method estimates a global model in which the PL parameters are represented as functions of t...

When two terms occur together in a document, the probability of a close relationship between them and the document itself is greater if they are in nearby positions. However, ranking functions including term proximity (TP) require larger indexes than traditional document-level indexing, which slows down query processing. Previous studies also show that this technique is not effective for all ty...

Rank breaking is a methodology introduced by Azari Soufiani et al. (2013a) for applying a Generalized Method of Moments (GMM) algorithm to the estimation of parametric ranking models. Breaking takes full rankings and breaks, or splits them up, into counts for pairs of alternatives that occur in particular positions (e.g., first place and second place, second place and third place). GMMs are of ...

Super eciency data envelopment analysis(DEA) model can be used in ranking the performanceof ecient decision making units(DMUs). In DEA, non-extreme ecient unitshave a super eciency score one and the existing super eciency DEA models do notprovide a complete ranking about these units. In this paper, we will propose a methodfor ranking the performance of the extreme and non-extreme ecient units.

Introduction: Failure Mode and Effects Analysis (FMEA) is a structured way to find and understand the states of a system’s failure and to calculate the resulting effects. In this method, which has been criticized by many researchers, the risk priority number is obtained for each failure mode based on the multiplication of the three parameters of occurrence (O), severity (S) and detection (D). I...

We show that several previously proposed passage-based document ranking principles, along with some new ones, can be derived from the same probabilistic model. We use language models to instantiate specific algorithms, and propose a passage language model that integrates information from the ambient document to an extent controlled by the estimated document homogeneity. Several document-homogen...

Two stages DEA models are used in many fields of management and industry. One of the concepts that has attracted the attention of researchers in the theory of production is the concept of ranking the units with a two-stage network. A unit ranking can provide useful information to decision makers (DMUs) about optimal decision making activities. This concept defines the superiority of a unit in t...