The book "Learning to Rank for Information Retrieval" by Te-Yan Liu, published by Springer, provides a comprehensive review of the major approaches to learning to rank in information retrieval. The author, from Microsoft Research Asia, highlights the importance of ranking technologies in search engines and other information retrieval applications. The book covers pointwise, pairwise, and listwise approaches, discussing their basic frameworks, example algorithms, and theoretical properties. It also introduces recent advances such as relational ranking, query-dependent ranking, semi-supervised ranking, and transfer ranking. The book includes benchmark datasets, practical issues like click-through log mining and training data selection, and real-world applications. Additionally, it delves into the theoretical guarantees for ranking performance and future research directions. The content is designed for researchers and graduate students in information retrieval and machine learning, with a self-contained introduction to relevant mathematical and machine learning concepts.The book "Learning to Rank for Information Retrieval" by Te-Yan Liu, published by Springer, provides a comprehensive review of the major approaches to learning to rank in information retrieval. The author, from Microsoft Research Asia, highlights the importance of ranking technologies in search engines and other information retrieval applications. The book covers pointwise, pairwise, and listwise approaches, discussing their basic frameworks, example algorithms, and theoretical properties. It also introduces recent advances such as relational ranking, query-dependent ranking, semi-supervised ranking, and transfer ranking. The book includes benchmark datasets, practical issues like click-through log mining and training data selection, and real-world applications. Additionally, it delves into the theoretical guarantees for ranking performance and future research directions. The content is designed for researchers and graduate students in information retrieval and machine learning, with a self-contained introduction to relevant mathematical and machine learning concepts.