Bilgi Erişimi için Eşli bir Sıralama Algoritması

Abstract

One of the main problems in machine learning is the determination of preference relations between interested units. In this context, ranking can be defined as learning a function with the ability to organize units according to a given preference relation. This type of problem is often treated as a classification problem where the examples are formed by pairs. In this study, an approach based on pairwise comparisons is presented for an estimation of a general ordering. This ranking problem that minimizes the pairwise ranking error is represented by a system of linear equations. An improved version of the gradient-descent algorithm is proposed to learn the ranking functions for solving this system of linear equations. In addition, Tikhonov regularization is also used to control the generalization performance of the ranking model. The developed rapid gradient descent algorithm converges to the solution in a very short time, regardless of the regularization level.