For personalized ranking models, the well-calibrated probability of an item being preferred by a user has great practical value. While existing work shows promising results in image classification, calibration not been much explored for ranking. In this paper, we aim to estimate calibrated how likely will prefer item. We investigate various parametric distributions and propose two methods, name...

in many applications, ranking of decision making units (dmus) is a problematic technical task procedure to decision makers in data envelopment analysis (dea), especially when there are extremely efficient dmus. in such cases, many dea models may usually get the same efficiency score for different dmus. hence, there is a growing interest in ranking techniques yet. the purpose of this paper is ra...

A considerable amount of research has addressed the methods and objectives of model combination. Very little attention has been given to the question of how to obtain a good collection of models for combination. Here a rationale for inductive inference of multiple models of time series is developed in terms of algorithmic information theory. A model-based Kolmogorov sufficient statistic is desc...

This work concerns learning probabilistic models for ranking data in a heteroge-neous population. The specific problem we study is learning the parameters of aMallows Mixture Model. Despite being widely studied, current heuristics for thisproblem do not have theoretical guarantees and can get stuck in bad local optima.We present the first polynomial time algorithm which provably...

Regarding the fact that getting a suitable combination of the human resources and service stations is one of the important issues in the most service and manufacturing environments, In this paper, we have studied the two models of planning queuing systems and its effect on the cost of the each system by using two fuzzy queuing models of M/M/1 and M/E2/1. In the first section, we have compar...

In many applications, ranking of decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA), especially when there are extremely efficient DMUs. In such cases, many DEA models may usually get the same efficiency score for different DMUs. Hence, there is a growing interest in ranking techniques yet. The purpose of this paper is ra...

Data envelopment analysis (DEA) with considering the best condition for each decision making unit (DMU) assesses the relative efficiency for it and divides a homogenous group of DMUs in to two categories: efficient and inefficient, but traditional DEA models can not rank efficient DMUs. Although some models were introduced for ranking efficient DMUs, Franklin Lio & Hsuan peng (2008), proposed a...