Input Ranking Model

Input ranking models aim to order items (e.g., search results, recommendations) based on relevance or user preference, often leveraging user feedback data like clicks. Current research emphasizes addressing biases in this feedback (e.g., position bias) through techniques like dual learning algorithms and counterfactual learning, as well as improving model robustness and generalization across diverse datasets and user behaviors. These advancements are crucial for enhancing the accuracy and fairness of ranking systems across various applications, from web search to personalized services.