Learning to Rank for Information Retrieval

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 engin...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Liu, Tie-Yan (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2011.
Edición:1st ed. 2011.
Temas:
Information storage and retrieval systems.
Artificial intelligence.
Computer science-Mathematics.
Mathematical statistics.
Pattern recognition systems.
Information Storage and Retrieval.
Artificial Intelligence.
Probability and Statistics in Computer Science.
Automated Pattern Recognition.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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