Recurrent convolutional networks for session-based recommendations

Abstract

Traditional recommender systems help users find items of interest by modeling long-term user profiles, which consist of items the users interacted with in the past. In many real-life applications, however, informative user profiles are not available. Instead, the system recommends by relying on the current user activities within an ongoing session, leading to the emergence of session-based recommendation methods. Among techniques used in such situations, recurrent neural networks (RNNs) present a natural choice thanks to their ability to model the order of session events and capture long-term dependencies. Recently, methods based on convolutional neural networks (CNNs) have also shown their potential in modeling session data, especially in extracting complex local patterns that are predictive of the user target. In this work, we propose a recurrent convolutional architecture that takes the advantages of both complex local features extracted by CNNs and long-term dependencies learned by RNNs from session sequences. Our model has two main layers: the lower layer consists of convolutional filters applied over consecutive session event embeddings, and the upper layer is a gated recurrent unit (GRU) RNN that takes as input the CNN’s output. This hybrid approach provides a flexible and unified network architecture for modeling various important features of session sequences. Experiments conducted on three benchmark datasets demonstrate the superiority of the proposed model over pure RNNs and CNNs models as well as state-of-the-art session-based recommendation methods.