Alleviating the Matthew Effect in O2O Service Matching Process
Yuying Yang, Xiao Xue, Fozhi Hou, Shizhan Chen, Zhiyong Feng, Lejun Zhang · 2021
With the development of Online to Offline (O2O) model and the rapid growth of service types and numbers, service matching algorithms have become the key in connecting users and services. The traditional service matching algorithms lack consideration for the limited resources of O2O services, leading to the Matthew effect more seriously. In this context, how to alleviate the Matthew effect through the optimization of matching algorithms has become an urgent problem in this field. Based on this, this paper proposes an adaptive optimization algorithm of O2O service matching to achieve the balance of supply and demand by optimizing supply, thus alleviating the Matthew effect. In addition, a computational experiment system is constructed to verify the effect of different matching algorithms on alleviating the Matthew effect. The result shows that our proposed algorithm can provide new means and ideas for alleviating the Matthew effect.