Learning to Rank for Information Retrieval by Tie-Yan Liu is a comprehensive book that reviews major approaches to learning to rank, including pointwise, pairwise, and listwise methods. It also covers advanced topics such as relational ranking, query-dependent ranking, semi-supervised ranking, and transfer ranking. The book discusses benchmark datasets like LETOR and other datasets used in learning to rank research. It addresses practical issues in learning to rank, such as data preprocessing, training data selection, and feature extraction. The book also explores applications of learning to rank in areas like question answering, multimedia retrieval, text summarization, and online advertising. It provides theoretical discussions on ranking performance guarantees and future research directions in learning to rank. The book is written for researchers and graduate students in information retrieval and machine learning, and includes a self-contained introduction to relevant knowledge in chapters 21 and 22. It aims to provide representative references and inspire future research in this fast-growing area. The book is structured into parts covering an overview of learning to rank, major approaches, advanced topics, benchmark datasets, practical issues, and theoretical aspects. It includes exercises and references for each chapter.Learning to Rank for Information Retrieval by Tie-Yan Liu is a comprehensive book that reviews major approaches to learning to rank, including pointwise, pairwise, and listwise methods. It also covers advanced topics such as relational ranking, query-dependent ranking, semi-supervised ranking, and transfer ranking. The book discusses benchmark datasets like LETOR and other datasets used in learning to rank research. It addresses practical issues in learning to rank, such as data preprocessing, training data selection, and feature extraction. The book also explores applications of learning to rank in areas like question answering, multimedia retrieval, text summarization, and online advertising. It provides theoretical discussions on ranking performance guarantees and future research directions in learning to rank. The book is written for researchers and graduate students in information retrieval and machine learning, and includes a self-contained introduction to relevant knowledge in chapters 21 and 22. It aims to provide representative references and inspire future research in this fast-growing area. The book is structured into parts covering an overview of learning to rank, major approaches, advanced topics, benchmark datasets, practical issues, and theoretical aspects. It includes exercises and references for each chapter.