Explicit Feedback Based Recommender System

Explicit feedback-based recommender systems aim to improve recommendations by incorporating user responses and preferences directly into the system's learning process. Current research emphasizes enhancing these systems' robustness and accuracy through techniques like adaptive loss functions that address data imbalances, the integration of large language models for improved representation learning and feedback generation, and the development of methods to mitigate biases arising from feedback loops. This field is significant because it directly impacts the quality and relevance of recommendations across diverse applications, from medical image analysis to personalized education and entertainment.