TAP: A Transformer based Activity Prediction Exploiting Temporal Relations in Collaborative Tasks

Activity prediction is an important challenge to provide cognitive supports in a smart space. For user activity prediction without privacy and inconsistent data collection issues, recent studies leverage data stream generated by ambient sensors. They use a deep learning model for single-user tasks which only contain simple sequential relations between activities. In this paper, we propose TAP, a Transformer-based activity prediction approach for inferring the next activity in a collaborative smart environment where collaborative tasks are conducted. To represent and analyze complex relations between activities of users, Allen's temporal relations are employed for representing temporal relations between activities and we leverage the Transformer to predict not only the next activity but also temporal relation with its preceding activity. TAP yields higher accuracy for the next activity prediction and temporal relations with the current activity in a collaborative task than existing approaches.