Annually, web search engine providers spend more and more money on documents ranking in search engines result pages (SERP). Click models provide advantageous information for ranking documents in SERPs through modeling interactions among users and search engines. Here, three modules are employed to create a hybrid click model; the first module is a PGM-based click model, the second module in a d...

University rankings generally present users with the problem of placing the results given for an institution in context. Only a comparison with the performance of all other institutions makes it possible to say exactly where an institution stands. In order to interpret the results of the SCImago Institutions Ranking (based on Scopus data) and the Leiden Ranking (based on Web of Science data), i...

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...

  In this paper, presenting two simple methods for ranking of efficient DMUs in DEA models that included to add one virtual DMU as ideal DMU and is using the additive model. Note that, we use an ideal point just for comparing efficient DMUs with. Although these methods are simple, they have ability for ranking all efficient DMUs, extreme points and the others, also they are capable of ranking t...

An adaptation process is described to adapt a ranking model constructed for a broad-based search engine for use with a domain-specific ranking model. It’s difficult to applying the broad-based ranking model directly to different domains due to domain differences, to build a unique ranking model for each domain it time-consuming for training models. In this paper,we address these difficulties by...

A novel ranking method based on multi-time information fusion is proposed for intuitionistic fuzzy sets (IFSs) and applied to the threat assessment problem, a multi-attribute decision making (MADM) one. This method integrates a designed intuitionistic fuzzy entropy (IFE), the closeness degree of technique for order preference by similarity to ideal solution (TOPSIS), the decision maker¡¯s (DM¡¯...

this paper introduces a new method for incorporating the performance of alternatives into their ultimate ranking in a linear assignment method. this method is one of multi-criteria decision-making methods, in which the weight of criteria is incorporated in ranking the alternatives, and the distance between the performances of alternatives do not affect in ranking the alternatives. in order to m...

Ranking documents in terms of their relevance to a given query is fundamental to many real-life applications such as document retrieval and recommendation systems. Extensive studies in this area have focused on developing efficient ranking models. While ranking models are usually trained based on given training datasets, besides model training algorithms, the quality of the document features se...

This paper regards the query keywords selection problem in source retrieval as learning a ranking model to choose the method of keywords extraction over suspicious document segments. Four basic methods are used in our ranking function: BM25, TFIDF, TF and EW. Then, a ranking model based on Ranking SVM is proposed to rank the query keywords group which is contributed to get the higher evaluation...