Abstract Ranking models are the main components of information retrieval systems. Several approaches to ranking based on traditional machine learning algorithms using a set hand-crafted features. Recently, researchers have leveraged deep in retrieval. These trained end-to-end extract features from raw data for tasks, so that they overcome limitations A variety been proposed, and each model pres...

Abstract As a task of high importance for recommender systems, we consider the problem learning convex combination ranking algorithms by online machine learning. First, propose stochastic optimization algorithm that uses finite differences. Our new achieves close to optimal empirical performance two base rankers, while scaling well with an increased number models. In our experiments five real-w...

The main purpose of this paper is to propose an approach for performance measurement, classification and ranking the investment companies (ICs) by considering internal structure and uncertainty. In order to reach this goal, the interval network data envelopment analysis (INDEA) models are extended. This model is capable to model two-stage efficiency with intermediate measures i...

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.

DEA Classic models cannot be used for inaccurate and indeterminate data, and it is supposed that the data for all inputs and outputs are accurate and determinate. However, in real life situations uncertainty is more common. This article attempts to get the common weights for Decision-Making Units by developing DEA multi-objective models in the grey environment. First, we compute the privilege o...

DEA Classic models cannot be used for inaccurate and indeterminate data, and it is supposed that the data for all inputs and outputs are accurate and determinate. However, in real life situations uncertainty is more common. This article attempts to get the common weights for Decision-Making Units by developing DEA multi-objective models in the grey environment. First, we compute the privilege o...

this paper uses integrated data envelopment analysis (dea) models to rank all extreme and non-extreme efficient decision making units (dmus) and then applies integrated dea ranking method as a criterion to modify genetic algorithm (ga) for finding pareto optimal solutions of a multi objective programming (mop) problem. the researchers have used ranking method as a shortcut way to modify ga to d...