Interaction Sequence

Interaction sequence analysis focuses on understanding and modeling patterns within sequences of events, aiming to predict future interactions or improve decision-making based on past behavior. Current research emphasizes efficient algorithms, such as variations of transformer networks and state-space models (like Mamba), to handle the computational challenges of long sequences and dynamic systems, often incorporating self-supervised learning and attention mechanisms to improve accuracy and robustness. This field is crucial for advancements in recommendation systems, dynamic graph learning, and robot control, offering significant potential for improving personalized experiences and automating complex tasks.