Nowadays social media are important tools for web resource discovery. The performance and capabilities of web searches are vital, especially search results from social research paper bookmarking. This paper proposes a new algorithm for ranking method that is a combination of similarity ranking with paper posted time or CSTRank. The paper posted time is static ranking for improving search result...

Ranking fuzzy numbers plays a very important role in the decision process, data analysis, and applications. The last few decades have seen a large number of methods investigated for ranking fuzzy numbers. The most commonly used approach for ranking fuzzy numbers is ranking indices based on the centroids of fuzzy numbers. However, there are some weaknesses associated with these indices. This pap...

The Mass Volume (MV) curve is a visual tool to evaluate the performance of a scoring function with regard to its capacity to rank data in the same order as the underlying density function. Anomaly ranking refers to the unsupervised learning task which consists in building a scoring function, based on unlabeled data, with a MV curve as low as possible at any point. In this paper, it is proved th...

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...

The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in machine learning. We study generalization properties of ranking algorithms, in a particular setting of the ranking problem known as the bipartite ranking problem, using the notion of algorithmic stability. In particular,...

We study generalization properties of ranking algorithms in the setting of the k-partite ranking problem. In the k-partite ranking problem, one is given examples of instances labeled with one of k ordered ‘ratings’, and the goal is to learn from these examples a real-valued ranking function that ranks instances in accordance with their ratings. This form of ranking problem arises naturally in a...

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 u...

‎ranking fuzzy numbers is generalization of the concepts of order, and class, and so have fundamental applications. moreover, deriving the final efficiency and powerful ranking are helpful to decision makers when solving fuzzy problems. selecting a good ranking method can apply to choosing a desired criterion in a fuzzy environment. there are numerous methods proposed for the ranking of fuzzy n...