Interaction Data

Interaction data, encompassing diverse forms of user or agent engagement, is increasingly used to understand complex systems and predict future behavior. Current research focuses on developing sophisticated models, such as transformers and temporal point processes, to analyze this data, often incorporating unsupervised learning techniques like clustering and dimensionality reduction to uncover hidden patterns within sequential and multi-agent interactions. This work spans various applications, from improving robot-human interaction and personalized recommendations to enhancing educational simulations and understanding social dynamics, highlighting the broad significance of interaction data analysis across multiple scientific disciplines and practical domains. The development of explainable models and the creation of diverse, high-quality datasets are key challenges driving ongoing research.