Tie-Yan Liu. I would like to dedicate this book to my wife and my lovely baby son! . In this part, we will give an overview of learning to rank for information retrieval.
Tie-Yan Liu. I would like to dedicate this book to my wife and my lovely baby son! Acknowledgements.
In addition, ranking is also pivotal for many other information retrieval applications, such as. .He leads a team working on learning to rank for information retrieval, and graph-based machine learning.
He leads a team working on learning to rank for information retrieval, and graph-based machine learning.
Discovery of context-specific ranking functions for effective information retrieval using genetic programming. IEEE Transactions on Knowledge and Data Engineering, 16(4):523-527, 2004. W. Fan, M. Gordon, and P. Pathak. A generic ranking function discovery framework by genetic programming for information retrieval. Information Processing and Management, 40(4):587-602, 2004. In SIGIR ’07 Workshop on learning to rank for information retrieval, 2007. Liu, T. Qin, Z. Ma, and H. Li. Supervised rank aggregation.
Intensive studies have been conducted on the problem recently and signicant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work. I would like to express my sincere gratitude to my colleagues Tie-Yan Liu, Jun Xu, Tao Qin, Yunbo Cao, and Yunhua Hu. We have been working together on learning to rank.
He leads a team working on learning to rank for information retrieval, and graph-based machine learning
He leads a team working on learning to rank for information retrieval, and graph-based machine learning. So far, he has more than 70 quality papers published in referred conferences and journals, including SIGIR(9), WWW(3), ICML(3), KDD, NIPS, ACM MM, IEEE TKDE, SIGKDD Explorations, etc.
Ranking is the central problem for information retrieval, and employing . This paper is concerned with learning to rank for information retrieval (IR).
Ranking is the central problem for information retrieval, and employing machine learning techniques to learn the ranking function is viewed as a promising approach to I.
Learning to Rank for Information Retrieval. Most Cited Paper Award (2004~2006). Author of two books on learning to rank. Introduction, Major Approaches to Learning to Rank, Analysis of the Approaches, Benchmark Evaluation of the Approaches, Statistical Learning Theory for Ranking, Summary and Outlook.
Tie-Yan Liu (2009), "Learning to Rank for Information Retrieval" .
Tie-Yan Liu (2009), "Learning to Rank for Information Retrieval", Foundations and Trends® in Information Retrieval: Vol. 3: No. 3, pp 225-331. Published: 27 Jun 2009.
Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people.
The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”.
Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches – these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance.
This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.