Traditional mechanisms used to rank learning objects are not longer viable thanks to the current abundance of resources. This work proposes the construction of an improved relevance ranking function based on contextualized attention data extracted from the interaction between the users and the objects. A multidimensional approach to relevance is followed. Beside the Algorithmic relevance, curre...

Jahanshahloo has suggested a method for the solving linear programming problems with zero-one variables‎. ‎In this paper we formulate fully fuzzy linear programming problems with zero-one variables and a method for solving these problems is presented using the ranking function and also the branch and bound method along with an example is presented.

A new explanation, using exponential functions, is given for the S-shaped functional relation between the mean citation score and the proportion of top 10% (and other percentages) publications for the 500 Leiden Ranking universities. With this new model we again obtain an explanation for the concave or convex relation between the proportion of top 100θ% publications, for different fractions of θ.

We consider the problem of learning the ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on training data. Relying on an 2-exact approximation for the error-function, we reduce the computational complexity of each iteration of a conjugate gradient algorithm for learning ranking functions from O(m2), to O(m), where m is the size of the training data. Experi...

Top-k ranking for an uncertain database is to rank tuples in it so that the best k of them can be determined. The problem has been formalized under the unified approach based on parameterized ranking functions (PRFs) and the possible world semantics. Given a PRF, one can always compute the ranking function values of all the tuples to determine the top-k tuples, which is a formidable task for la...

Justsystem participated in Patent Classification Subtask at the Fifth NTCIR workshop. This paper overviews our machine learning-based patent application classification system. Straightforward application of Naive Bayes classifier was effective in theme categorization subtask that has a non-hierarchical category structure. In F-term categorization subtask, we regarded the complicated F-term cate...

Group decision is usually understood as the reduction of different individual preferences on a given set to a single collective preference. At present, there are few approaches which solve the group ranking problem with multiple criteria in a widely acceptable way. Often, they rest on a poor heuristic which makes a decision about consensus ranking difficult to support. This paper presents an ex...

The dramatic growth in the number of application domains that naturally generate probabilistic, uncertain data has resulted in a need for efficiently supporting complex querying and decision-making over such data. In this paper, we address the problem of on-the-fly clustering and ranking over probabilistic databases. We begin with a systematic exploration of ranking in probabilistic databases b...

In many information retrieval systems such as Web search engines and biological-sequence search engines, the ranking functions that list the search results in order of their relevances to the query are one of the most important components. In the machine learning approaches to constructing ranking-functions, the feature vectors of database items are computed based on queries and thus they are g...

In this paper we turn the spotlight on a class of lexicographic ranking functions introduced by Bradley, Manna and Sipma in a seminal CAV 2005 paper, and establish for the first time the complexity of some problems involving the inference of such functions for linear-constraint loops (without precondition). We show that finding such a function, if one exists, can be done in polynomial time in a...