Prediction of Online Consumers’ Repeat Purchase Behavior via BERT-MLP Model

Abstract

It is an effective means for merchants to carry out precision marketing and improve ROI by using historical user behavior data obtained from promotional activities in order to build a model to predict the repeat purchase behavior of users after promotional activities. Most of the existing prediction models are supervised learning, which does not work well with a small amount of labeled data. This paper proposes a BERT-MLP prediction model that uses “large-scale data unsupervised pre-training + small amount of labeled data fine-tuning.” The experimental results on Alibaba real dataset show that the accuracy of the BERT-MLP model is better than the baseline model.