Ranking search results is an ongoing research topic in information retrieval. The traditional models are the vector space, probabilistic and language models, and more recently machine learning has been deployed in an effort to learn how to rank search results. Categorization of search results has also been studied as a means to organize the results, and hence to improve users search experience....

For ambiguous queries, conventional retrieval systems are bound by two conflicting goals. On the one hand, they should diversify and strive to present results for as many query intents as possible. On the other hand, they should provide depth for each intent by displaying more than a single result. Since both diversity and depth cannot be achieved simultaneously in the conventional static retri...

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...

We investigate the relationship between three fundamental problems in machinelearning: binary classification, bipartite ranking, and binary class probability esti-mation (CPE). It is known that a good binary CPE model can be used to obtain agood binary classification model (by thresholding at 0.5), and also to obtain a goodbipartite ranking model (by using the CPE model dire...

Supplier selection is a multi-Criteria problem. This study proposes a hybrid model for supporting the suppliers’ selection and ranking. This research is a two-stage model designed to fully rank the suppliers where each supplier has multiple Inputs and Outputs. First, the supplier evaluation problem is formulated by Data Envelopment Analysis (DEA), since the regarded decision deals with uncertai...

The emergence of the deep Web databases have given a new connotation to the concept of ranking query results. Earlier approaches for ranking resorted to analyzing frequencies of database values and query logs or establishing user profiles. In contrast, an integrated approach, based on the notion of a similarity model, for supporting useras well as query-dependent ranking has been recently propo...

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 define a model of learnability for ranking functions in a particular setting of the ranking problem known as the bipartite ranking problem, and derive a number of results in this model. Our first mai...

BACKGROUND In medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statisti...

We present a machine learning approach for the task of ranking previously answered questions in a question repository with respect to their relevance to a new, unanswered reference question. The ranking model is trained on a collection of question groups manually annotated with a partial order relation reflecting the relative utility of questions inside each group. Based on a set of meaning and...

Background. Although the World Taekwondo federation currently applies the APIS ranking method to calculate the Olympic rankings, some limitations exist. Objectives. This study applies the PageRank model to Olympics Taekwondo rankings. Methods. The 2015-2018 World Taekwondo Grand Prix competition results for women’s four weight classes (-49kg, -57kg, -67kg, +67kg) were used as research data, t...