Learning to Rank

Learning to Rank (LTR) is a machine learning technique focused on ordering items based on relevance to a given query, crucial for applications like search engines and recommender systems. Current research emphasizes addressing challenges like cold starts (lack of initial user data), non-stationary user behavior, and ensuring consistent performance across different data scales, employing models such as Bayesian networks, deep neural networks (including Transformers and Graph Neural Networks), and gradient boosted decision trees. LTR's impact spans various fields, improving the efficiency and fairness of information retrieval, recommendation systems, and even aiding in tasks like medical image analysis and legal case retrieval.