Semantic ranking models go beyond keyword matching to score documents based on closeness in meaning to the query. The use of semantic ranking in Web search has been limited due to the high cost of these models. To address this issue, we have designed and implemented a new Web-scale ranking system that enables us to integrate semantic ranking techniques into a commercial search engine. We have e...

in today's competitive world, establishıng a proper system of evaluating firms performance is essential. the purpose of this study is to evaluate the financial performance of pharmaceutical, basic metals and automotive parts companies using a new method. the fuzzy ahp is used for weighting the performance evaluation measures and fuzzy topsis and fuzzy vikor is also used to rank the compani...

  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...

Ranking of a company's financial information is one of the most important tools for identifying strengths and weaknesses and identifying opportunities and threats outside the company. In this study, it is attempted to examine the financial statements of companies to rank and explain the transparency of financial information of 198 companies during 2009-2017 using artificial intelligence and neu...

Conventional data envelopment analysis (DEA) assists decision makers in distinguishing between efficient and inefficient decision making units (DMUs) in a homogeneous group. However, DEA does not provide more information about the efficient DMUs. One of the interesting research subjects is to discriminate between efficient DMUs. The aim of this paper is ranking all efficient (extreme and non-ex...

In today’s big data era, huge amounts of ranking and choice data are generated on a daily basis, and consequently, many powerful new computational tools for dealing with ranking and choice data have emerged in recent years. This paper highlights recent developments in two areas of ranking and choice modeling that cross traditional boundaries and are of multidisciplinary interest: ranking from p...

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

Aggregate ranking tasks are those where documents are not the final ranking outcome, but instead an intermediary component. For instance, in expert search, a ranking of candidate persons with relevant expertise to a query is generated after consideration of a document ranking. Many models exist for aggregate ranking tasks, however obtaining an effective and robust setting for different aggregat...