Bilateral Matching Method for Business Resources Based on Synergy Effects and Incomplete Data

Abstract

On the third-party cloud platform, to help enterprises accurately obtain high-quality and valuable business resources from the massive information resources, a bilateral matching method for business resources, based on synergy effects and incomplete data, is proposed. The method first utilizes a k-nearest neighbor imputation algorithm, based on comprehensive similarity, to fill in missing values. Then, it constructs a satisfaction evaluation index system for business resource suppliers and demanders, and the weights of the satisfaction evaluation indices are determined, based on the fuzzy analytic hierarchy process (FAHP) and the entropy weighting method (EWM). On this basis, a bilateral matching model is constructed with the objectives of maximizing the satisfaction of both the supplier and the demander, as well as achieving the synergy effect. Finally, the model is solved using the linear weighting method to obtain the most satisfactory business resources for both supply and demand. The effectiveness of the method is verified through a practical application and comparative experiments.