Machine Learning Based Prediction and Prevention of Malicious Inventory Occupied Orders

Abstract

MIOOs are orders created temporarily for the purpose of occupying the inventories of sellers. MIOOs disrupt normal business activities and harm both sellers and consumers. This study aims to determine the best practice and model of the technical solutions that can effectively and systematically limit malicious inventory occupied orders (MIOOs), using the methods of analytical mining and case studies. This work contains three contributions. Firstly, this work solves MIOOs problem by using machine learning technology. The result of the study indicates that 93% of MIOOs from the sample data are actually predictable and preventable. Secondly, this work presents a methodology of solving MIOOs problem which can be applied by other companies. The methodology in this paper consists of four major steps, namely doing statistics concerning MIOOs, using logistic regression algorithm to train a mode, optimizing the model, and applying the model. Finally, this work finds unique features of MIOOs, and they can help better understanding the behind logic of MIOO producers.