“ranking framework” is correct and usable in written English.
You can use this term to refer to a structured approach to evaluate the relative importance of items within a categorical system, such as ranking different sports teams by their win/loss records. For example, “The rankings for professional basketball teams are determined by a points-based ranking framework.”.
A new link-based document ranking framework is devised with at its heart, a contents and time sensitive random literature explorer designed to more accurately model the behaviour of readers of scientific documents.
Developers can also add an extra ranking framework to stack search results.
In this article, we describe a new drug ranking framework called FABS.
We have shown the applicability of the ranking framework using a case study.
The novelty of this work is the formulation of the problem of subpixel motion estimation in a ranking framework.
In particular, our ranking framework dynamically adjusts its random walk parameters according to both contents and age of encountered documents, thus incorporating the diversity of topics and how they evolve over time into the score of a scientific publication.
For example, Kumaran and Carvalho [24] apply learning to rank framework in reducing long queries using query quality predictors, such as Query Clarity [13] and Query Scope [19].
For this purpose, we use the learning-to-rank framework in [18], especially designed for link prediction in large networks.
We took advantage of this for link prediction by using a learning-to-rank framework that may rank high items even if some features do not rank them high.
In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.
In particular, this paper proposes a novel integrated feature ranking and selection framework which combines information theoretic criteria and the least absolute shrinkage and selection operator (Lasso) method into a two-step feature selection process.
