Temporal Knowledge Graph Attention Network for Online Doctor Recommendation

作者：Shuang Geng, Bang Tao, Gemin Liang, Chao Fu, Wenli Zhang, Ben Niu · 年份：2023 · DOI：10.1145/3635175.3635224 · 被引用次数：3 · 研究领域：Recommender Systems and Techniques、Machine Learning in Healthcare、Advanced Graph Neural Networks

This paper discusses challenges in online doctor recommendation services, emphasizing personalized recommendations based on patients' health needs and preferences. We address issues related to information asymmetry and time-related attributes of diseases (e.g., seasonal, recurring, and long-term) by proposing the use of knowledge graphs and introducing the temporal factor into the KGAT approach in the online doctor recommendation scenario. We construct knowledge graphs using interaction data, extract heterogeneous auxiliary information to accurately assess user preferences, and encode timestamps to cluster temporal dynamics. Using a real-world dataset, we validate our approach and demonstrate its effectiveness in enhancing the performance of online doctor recommendation systems when compared to classical models used as a baseline for comparison.