A Comparative Study of Preference Ordering Methods for Multi-Criteria Ranking

Abstract

Multi-criteria recommender systems are capable of enhancing recommendation quality by taking into account user preferences across multiple criteria. A promising approach that has recently emerged is multi-criteria ranking, which employs Pareto ranking to determine a ranking score based on the dominance relation of predicted multi-criteria ratings. While this technique can be integrated with existing MCRS models, the issue of dimensionality remains a challenge. To tackle similar problems, other preference ordering methods have been proposed in the field of multi-objective optimization. This study presents a comparative analysis of preference ordering methods for multicriteria ranking, along with insights obtained from experiments conducted on four real-world datasets.