Spatiotemporal Graph Convolutional Neural Network-Based Text Recommendation by Considering Situational Awareness

Conventional text recommendation models often overlooked user contextual information, resulting in low accuracy and efficiency. Therefore, we propose a new model to integrate user spatiotemporal contextual information, so as to improve the recommendation effect. The study extracts spatiotemporal contextual information of users from their behavioral data, including location information, as well as behavioral characteristics. By processing textual semantic analysis, we define graph adjacency matrix and spatial dependencies. Hence, a spatiotemporal graph convolutional neural network model can be constructed to learn the correlation between users' spatiotemporal contextual features and text items. The proposed model can effectively capture changes in user preferences and interests towards text in different contexts, thereby leading to better recommendation results. After that, some experiments are conducted on real-world dataset to make performance evaluation. The experimental results show that the proposal can achieve significant improvement in recommendation efficiency compared with typical methods. This indicates that proposed model can better understand contextual user information, and improve the user experience in recommendation systems.