Dynamic Recommender System

Dynamic recommender systems aim to provide personalized recommendations that adapt to users' evolving preferences and behaviors over time, addressing the limitations of static models. Current research focuses on developing models that efficiently handle continuous data streams, incorporating temporal information through graph networks and time-decay functions, and mitigating biases like popularity bias through debiased learning and fairness-aware algorithms. These advancements are crucial for improving the accuracy, robustness, and ethical implications of recommender systems across various applications, from e-commerce to personalized healthcare.