Best next preference prediction based on LSTM and multi-level Interactions

Predict customer buying behavior is an important task for improving direct marketing campaigns, offering the best possible experiences, and providing personalization in the customer journey trip. Improving how models capture the sequential information from transactional data is essential to learn the customer buying order and repetitive buying patterns to generate recommendations over time. In this paper, we propose the deep neural network approach DeepCBPP, which models the sequence prediction problem as a multi-class classification problem and takes the LSTM neural network as the base of the training process. Our main contributions rely on a new sequence customer representation approach based on multi-level interactions of the most recent influenced items, which allows predicting preferences without sophisticated feature engineering. The simulations using 12 datasets from a real-world problem achieve competitive results compared to the state-of-the-art sequence prediction models supporting the effectiveness of our proposal.