Product Classification With the Motivation of Target Consumers by Deep Learning

Due to the dynamic and competitive market environment, it is widely recognized that the development of new products and processes has become the critical point of attention for many companies. The first step in the product development process is to define the nature and function of the product, which is to classify the new product. The traditional product classification process only focuses on the product and market, which is developed by designers on the basis of the products of past dynasties. This way is labor-consuming, inefficient, and has become a bottleneck or constraint for these enterprises to improve their productivities and regulate production. In recent years, Artificial Intelligence has been applied in a wide range of fields including product classification. How to apply machine learning technologies to solve the classification problem of product classification has been widely concerned by researchers. In this paper, a fast and effective product classification, called MdmNet, is proposed, which is based on a novel attempt that embeds the innovation idea of human in machine learning technologies. MdmNet includes three modules: a target customer modeling method based on the deep learning technologies a consumer information deduction method based on the MDM that builds a consumer feature closed loop and output the classification result of the consumers' perspective, and a weighted fusion module. Experiments conducted on benchmark datasets Cars demonstrate the impressive performance of the proposed MdmNet. This paper first attempts to add consumer motivation analysis to traditional machine learning method, which has a strong application prospect.