Research On Consumer Behavior Based On Large Scale Of E-commerce Data

Under the background of today’s era of big data, user behavior analysis, especially users of e-commerce platform consumer behavior analysis is a hot academic research at home and abroad, and is widely used in engineering applications. User consumer behavior analysis helps electronic business platform to advertise precision of delivery, commodity personalized recommendation; electricity supplier Seller insight helps users of consumer psychology, consumer tracking user trends, thereby producing a user-friendly product, improve business profitability. Many scholars at home and abroad have online user behavior analysis study. Through the online user behavior analysis modeling constructs a number of applications including recommended system, social impact analysis, and achieved good results. The rise of e-commerce development in recent years, Hadoop and Spark and other large data and cloud computing technology allows user-based e-commerce platform consumer behavior analysis possible.We define consumer behavior based on two reasonable assumptions, which allow us do quantified research on consumption behavior. Under this definition, we model consumer behavior from three aspects. First, from the perspective of the user mathematical understanding consumer behavior and build probability formula consumption behavior, and through a number of independence so that we can assume probability formula to solve the user’s consumer behavior. Second, we analyzed the numerous shortcomings of the mathematical model and presents the user with naive Bayesian approach consumer behavior modeling method from the impact of commodity consumption characteristics of the user’s decision-making aspects. Thirdly, Naive Bayes methods for each commodity consumption characteristics affect the user’s decision to do a conditional independence assumption, this assumption has some limitations. Taking into account the consumption behavior compare with similar training neural network, we chose the neural network user consumption behavior modeling.Eventually we got the three models, due to the limitations of the first model in the experiment we only consider the second and third models. Experiment, we first obtained from a B2 C e-commerce formula data preprocessing, and then the two models for training and testing. Overall, Naive Bayesian approach to the user consumer behavior prediction accuracy rate can reach 70%, while the neural network model of consumption behavior prediction accuracy rate can reach 85%.