Task Preference

Task preference research investigates how individuals prioritize and select among different tasks, focusing on aligning automated systems with human needs and optimizing multi-task performance. Current research explores methods like multi-task learning with shared and task-specific embeddings, and conditional diffusion models guided by preference representations, to improve the efficiency and effectiveness of task allocation and execution across diverse domains. This work has implications for improving recommender systems, natural language processing, and human-robot interaction, particularly in assisting older adults or tailoring online platforms to diverse linguistic communities.