Application of Long Short-term Memory Based Neural Network for Classification of Customer Behavior

In this article, we use the method of machine learning to model the consumer behavior, in order to identify the consumers with high volume purchases, and make a quantitative analysis of their behavior patterns. Firstly, radio frequency identification (RFID) sensors are installed in physical supermarkets for data sampling, tracking the shopping behavior of consumers in supermarkets, combining them with point of sale (POS) data at checkout, and transferring them to consumer behavior data set. Then, based on POS data, we mark consumers as high volume, low volume and none volume customer groups with historical purchase volume as target variables. Based on RFID data, stay time, shopping background, flow direction and number of return visits are extracted as explanatory variables. In the experiment, a classification model of consumer behavior is established by using long short-term memory based neural network (LSTM-NN). Numerical experiments show that LSTM-NN has higher recognition accuracy than logistic regression and support vector machine, which are improved by 5.26% and 6.97% respectively.