Product Customer Demand Mining and Its Functional Attribute Configuration Driven by Big Data

Abstract

The maturity of big data analysis theory and its tools improve the efficiency and reduce the cost of massive data mining. This paper discusses the method of product customer demand mining based on big data, and further studies the configuration of product function attributes. Firstly, the Hadoop platform was used to perform product attribute data participle and feature word extraction based on Apriori algorithm was used to mine product customer demand information. And then the MapReduce model on the big data platform was applied into efficient parallel data processing, obtaining product attributes with research value, and their weights and attribute levels. After that, the cloud model and the MNL model were employed to construct the product function attribute configuration model, and the improved artificial bee colony algorithm was used to solve the model. The optimal solution of the product function attribute configuration model was got. Finally, an example was given to illustrate the feasibility of the proposed method in this paper.