Affinity Degree as Ranking Method

Abstract

In machine learning, ranking is a fundamental problem that attempts to rank a list of things based on their relevance in a certain task. Ranking can be helpful, especially for future decision making. The framework for ranking has been classified into three primary approaches in machine learning: pointwise, pairwise, and listwise. However, learning to rank in all three approaches still lacks continuous learning ability, particularly when it comes to determining the degree of relevancy of ranking orders. In this paper, an affinity degree technique for ranking is proposed as another potential machine learning framework. The definition and attributes of the affinity degree technique are discussed, as well as the results of an experiment adopting the affinity degree approach as a ranking mechanism. The experiment's performance is measured using assessment metrics such as Mean Average Precision (MAP).