Predicting Tasks in Social Groups Using a Gated Recurrent Unit

Abstract

we studied task prediction for informal groups (families, friends, interest groups, etc.) in collaborative planning systems. We developed a method based on a gated recurrent unit (GRU), chosen as a more efficient and robust neural network for short sequences than standard recurrent neural networks or long short-term memory networks. We implemented a prediction algorithm that considers both individual behavioral patterns and the collective dynamics of the group, enabling the identification of personalized and collaborative tasks. We evaluated the model using the F1-score, which is appropriate for this problem because it reduces to a binary classification (task / no task) with a strong class imbalance: most time intervals contain no new tasks, and positive examples are rare. The F1-score, as the harmonic mean of precision and recall, provides a balanced assessment by accounting equally for false positives and false negatives, since missing a real task is as undesirable as generating a false prediction.