000 03879nam a22004815i 4500
001 978-3-642-14267-3
003 DE-He213
005 20140220083745.0
007 cr nn 008mamaa
008 110429s2011 gw | s |||| 0|eng d
020 _a9783642142673
_9978-3-642-14267-3
024 7 _a10.1007/978-3-642-14267-3
_2doi
050 4 _aQA75.5-76.95
072 7 _aUNH
_2bicssc
072 7 _aUND
_2bicssc
072 7 _aCOM030000
_2bisacsh
082 0 4 _a025.04
_223
100 1 _aLiu, Tie-Yan.
_eauthor.
245 1 0 _aLearning to Rank for Information Retrieval
_h[electronic resource] /
_cby Tie-Yan Liu.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2011.
300 _aXX, 292p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1. 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.
520 _aDue 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.
650 0 _aComputer science.
650 0 _aInformation storage and retrieval systems.
650 0 _aArtificial intelligence.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aPattern Recognition.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642142666
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-14267-3
912 _aZDB-2-SCS
999 _c106942
_d106942