00000cmm a22000004i 4500 UP-99796217611135031 Buklod 20140228110009.0 m go j cr |n |||auu|a 110429s2011 xx u | eng d 9783642142673 (eBook) 3642142672 (eBook) (iLib)UPD-00214604022 DLC DML eng DMLUC QA 76.9 A43 L58 2011eb Liu, Tie-Yan. Learning to rank for information retrieval [electronic resource] Tie-Yan Liu. Berlin Heidelberg Springer 2011. 1 online resource (xx, 292 p.) 1. Ranking in IR -- 2. Learning to Rank for IR -- 3. Regression/Classification: Conventional ML Approach to Learning to Rank -- 4. Ordinal Regression: A Pointwise Approach to Learning to Rank -- 5. Preference Learning: A Pairwise Approach to Learning to Rank -- 6. Listwise Ranking: A Listwise APproach to Learning to Rank -- 7. Advanced Topics -- 8. LETOR: A Benchmark Dataset for Learning to Rank -- 9. Summary and Outlook. IP-based subscription, access limited to within on-campus computer network. Access via Electronic Resources of the UPD University Library Website. 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. Electronic reproduction. New York SpringerLink c2011. Available via World Wide Web through SpringerLink. Computer algorithms. Information retrieval. Electronic books. SpringerLink (Online service). Available for University of the Philippines Diliman via SpringerLink. Click here to access http://link.springer.com/book/10.1007/978-3-642-14267-3 (viewed 25 July 2014) FO Monograph UPD DMLR Electronic Resource