{
"item_title" : "Learning to Rank for Information Retrieval",
"item_author" : [" Tie-Yan Liu "],
"item_description" : "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.",
"item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/64/214/266/3642142664_b.jpg",
"price_data" : {
"retail_price" : "159.99", "online_price" : "159.99", "our_price" : "159.99", "club_price" : "159.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : ""
}
}
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.
This item is Non-Returnable
Customers Also Bought
Details
- ISBN-13: 9783642142666
- ISBN-10: 3642142664
- Publisher: Springer
- Publish Date: May 2011
- Dimensions: 9.2 x 6.2 x 0.8 inches
- Shipping Weight: 1.2 pounds
- Page Count: 285
Related Categories
