Machine Learning Model for Sparse PCM Completion

TL;DR

This paper introduces a machine learning model that enhances the completion of sparse pairwise comparison matrices by integrating classical methods with graph-based learning, demonstrating improved effectiveness and scalability.

Contribution

The paper presents a novel approach combining classical PCM techniques with graph-based learning for better sparse PCM completion.

Findings

Effective completion of sparse PCMs demonstrated

Scalable approach suitable for large datasets

Improved accuracy over traditional methods

Abstract

In this paper, we propose a machine learning model for sparse pairwise comparison matrices (PCMs), combining classical PCM approaches with graph-based learning techniques. Numerical results are provided to demonstrate the effectiveness and scalability of the proposed method.