A product recommendation model based on recurrent neural network

Abstract

In traditional recommender systems, the product recommendations are generally made based on the static behavior or preference of customers. This paper designs a novel production recommendation model that processes customer-product interaction data as a time-based sequential data, and makes personalized product recommendations based on the purchase patterns of customers. Specifically, the model relies on the deep learning technique of recurrent neural network (RNN) to uncover the dynamics in purchase patterns of customers; a bidirectional model with attention mechanism was introduced to personalize the product recommendations. The effectiveness of the proposed model was verified through an experiment on a benchmark dataset called Movie lens. The experimental results show that the RNN-based model can efficiently capture the temporal dynamics of customer preferences, and then generate highly individualized product recommendations.