Learning to Rank for Information Retrieval

• Main Author: Liu, Tie-Yan (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2011.
• Subjects: Computer science.; Mathematical statistics.; Information storage and retrieval.; Artificial intelligence.; Pattern recognition.; Computer Science.; Information Storage and Retrieval.; Artificial Intelligence (incl. Robotics).; Probability and Statistics in Computer Science.; Pattern Recognition.

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...

Table of Contents:

• 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.