Performance-driven contractor recommendation system using a weighted activity-contractor network

The reliance of contractor selection for specific construction activities on subjective judgments remains a complex decision-making process with high stakes due to its impact on project success. Existing methods of contractor selection lack a data-driven decision-support approach, leading to suboptimal contractor assignments. Here, an advanced node2vec-based recommendation system is proposed that addresses the shortcomings of conventional contractor selection by incorporating a broad range of quantitative performance indicators. This study utilizes semi-supervised machine learning to analyze contractor records, creating a network in which nodes represent activities and weighted edges correspond to contractors and their performances, particularly cost and schedule performance indicators. Node2vec is found to display a prediction accuracy of 88.16% and 84.08% when processing cost and schedule performance rating networks, respectively. The novelty of this research lies in its proposed network-based, multi-criteria decision-making method for ranking construction contractors using embedding information obtained from quantitative contractor performance data and processed by the node2vec procedure, along with the measurement of cosine similarity between contractors and the ideal as related to a given activity.