Page Wise Feedback

Page-wise feedback, encompassing both positive and negative user interactions within a specific context (e.g., a webpage or dialogue turn), is a rapidly developing area of research aiming to improve the accuracy and relevance of machine learning models. Current work focuses on incorporating this contextual feedback into model training, often using techniques like contrastive learning, reinforcement learning with fine-grained supervision, and recurrent attention mechanisms to capture sequential patterns in user behavior. This research is significant because it allows for more nuanced and effective model adaptation, leading to improvements in areas such as personalized recommendations, robotic control, and human-computer interaction.