QPIN: A Quantum-inspired Preference Interactive Network for E-commerce Recommendation

Abstract

Recently, recurrent neural networks (RNNs) based methods have achieved profitable performance on mining temporal characteristics in user behavior. However, user preferences are changing over time and have not been fully exploited in e-commerce scenarios. To fill in the gap, we propose an approach, called quantum inspired preference interactive networks (QPIN), which leverages the mathematical formalism of quantum theory (QT) and the long short term memory (LSTM) network, to interactively learn user preferences. Specifically, the tensor product operation is used to model the interaction among a single user's own preferences, i.e. individual preferences. A quantum many-body wave function (QMWF) is employed to model interaction among all users' preferences, i.e. group preferences. Further, we bridge them by deriving a rigorous projection, and thus take the interplay between them into account. Experiments on an Amazon dataset as well as a real-world e-commerce dataset demonstrate the effectiveness of QPIN, which achieves superior performances compared with the state-of-the-art methods in terms of AUC and F1-score.