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Learning to Rank for Information Retrieval|Tie-Yan Liu

Learning to Rank for Information Retrieval

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Overview

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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Details

  • ISBN-13: 9783642441240
  • ISBN-10: 3642441246
  • Publisher: Springer
  • Publish Date: September 2014
  • Dimensions: 9.21 x 6.14 x 0.64 inches
  • Shipping Weight: 0.94 pounds
  • Page Count: 285

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