Augmenting Recurrent Neural Networks with High-Order User-Contextual Preference for Session-Based Recommendation
This work addresses the problem of improving session-based recommendations for users by incorporating explicit user-side contexts, representing an incremental advancement over existing RNN methods.
The paper tackles the limitation of existing RNN-based session models in explicitly modeling static user-side contexts like age and gender, proposing an augmented RNN model that extracts high-order user-contextual preference using a product-based neural network. The result shows that this model outperforms the baseline RNN session model by a large margin when rich user contexts are available.
The recent adoption of recurrent neural networks (RNNs) for session modeling has yielded substantial performance gains compared to previous approaches. In terms of context-aware session modeling, however, the existing RNN-based models are limited in that they are not designed to explicitly model rich static user-side contexts (e.g., age, gender, location). Therefore, in this paper, we explore the utility of explicit user-side context modeling for RNN session models. Specifically, we propose an augmented RNN (ARNN) model that extracts high-order user-contextual preference using the product-based neural network (PNN) in order to augment any existing RNN session model. Evaluation results show that our proposed model outperforms the baseline RNN session model by a large margin when rich user-side contexts are available.