Dual Relations Network for Collaborative Filtering

Collaborative filtering (CF) is one of the most effective and popular recommendation methods. Deep learning has achieved great success in various tasks and extensive work has applied deep learning into collaborative filtering. However, existing deep learning based CF methods only capture either user-item relations (users' affection for target items) or item-item relations (association between target items and users' historical items), which may lead to sub-optimal recommendation accuracy. In this paper, we propose deep dual relations network, a neural network based approach that can model user-item relations and item-item relations simultaneously. Furthermore, considering the fact that a historical item a user prefers will have a more positive impact on his decision on a new item, user-item relations should play an important role in item-item relations modeling, which is not investigated by previous work. To solve this problem, we propose a special architecture, named affection-based attention network to capture the impact of user-item relations on item-item relations modeling. Comprehensive experimental results on four real-world datasets demonstrate the effectiveness of our proposed model.