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Learning to Rank for Information Retrieval
139,09 €*
ISBN-13:
9783642142673
Veröffentl:
2011
Seiten:
285
Autor:
Tie-Yan Liu
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch
Beschreibung:
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 author of this book first reviews the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms. Scientific theoretical soundness is combined with broad development and application experiences.
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.