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.
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