Exploiting Long- and Short-Term Preferences for Deep Context-Aware Recommendations

With the tremendous growth in online information, capturing dynamic users' preferences based on their historical interactions and providing a few desirable items to users have become an urgent service for all businesses. Recurrent neural networks (RNNs) and item-based collaborative filtering (CF) models are commonly used in industries due to their simplicity and efficiency. However, they fail to different contexts that could differently influence current users' decision-making. Also, they are not sufficient to capture multiple users' interests based on features of the interacting items. Besides, they have a limited modeling capability for the evolution of diversity and dynamic user preferences. In this article, we exploit long- and short-term preferences for deep context-aware recommendations (LSCAR) to enhance the next item recommendation's performance by introducing three novel components as follows: 1) the user-contextual interaction module is proposed to capture and differentiate the interaction between contexts and users; 2) the encoded multi-interest module is introduced to capture various types of user interests; and 3) the integrator fusion gate module is used to effectively fuse the related long-term interests to the current short-term part, and the module returns the final user interest representation. Extensive experiments and results for two public datasets demonstrate that the proposed LSCAR outperforms the state-of-the-art models in almost all metrics and could provide interpretable recommendation results.