Temporal attention graph-optimized networks for sequential recommendation - Pathak, Siddhant (Nanyang Technological University, 2024)
Sequential recommendation systems are pivotal in enhancing user experience by providing personalized suggestions based on historical data. However, traditional models often disregard the temporal dynamics of user-item interactions, which can lead to static and outdated recommendations that fail to reflect current user preferences. To address this critical gap, this study introduces a novel architecture known as Temporal Attention Graph-Optimized Networks (TAGON), which integrates Graph Neural Networks (GNNs) with an innovative temporal attention mechanism. TAGON is designed to dynamically adapt to both the sequential and temporal aspects of user interactions, thereby offering more accurate and relevant recommendations.