Preference Representation

Preference representation research focuses on effectively capturing and utilizing human preferences for various applications, primarily in aligning AI systems with human values and improving recommendation systems. Current research emphasizes learning rich, structured representations of preferences, moving beyond simple numerical reward models and exploring techniques like preference embedding, graph convolutional networks, and transformer architectures to handle complex, potentially intransitive preferences. This work is crucial for enhancing the safety, reliability, and user experience of AI systems, particularly large language models, and for developing more effective and personalized recommendation systems.