Delivery: Can be download immediately after purchasing. For new customer, we need process for verification from 30 mins to 12 hours.
Version: PDF/EPUB. If you need EPUB and MOBI Version, please send contact us.
Compatible Devices: Can be read on any devices
Learning to Rank for Information Retrieval eBook
$129.00 Original price was: $129.00.$30.00Current price is: $30.00.
By: Tie-Yan Liu
Publisher: Springer
Print ISBN: 9783642142666, 3642142664
eText ISBN: 9783642142673, 3642142672
Copyright year: 2011
eText ISBN: 9783642142673
SKU: 9783642142673
Category: Trending
Tags: Computers, Intelligence (AI) & Semantics
Print ISBN: 9783642142666
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 engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”. Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches – these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.
Be the first to review “Learning to Rank for Information Retrieval eBook” Cancel reply
You must be logged in to post a review.
Related products
-38%
Bestsellers
eText ISBN: 9781610395700
$19.98 Original price was: $19.98.$12.31Current price is: $12.31.
-38%
Bestsellers
eText ISBN: 9781119473879
$19.98 Original price was: $19.98.$12.31Current price is: $12.31.
-38%
eText ISBN: 9780735642577
$23.32 Original price was: $23.32.$14.37Current price is: $14.37.
-38%
Bestsellers
eText ISBN: 9781111787080
$51.65 Original price was: $51.65.$31.82Current price is: $31.82.
-38%
eText ISBN: 9781118890868
$76.65 Original price was: $76.65.$47.22Current price is: $47.22.
-38%
Bestsellers
eText ISBN: 9781482233902
$58.32 Original price was: $58.32.$35.93Current price is: $35.93.
-38%
Bestsellers
eText ISBN: 9781423207733
$6.65 Original price was: $6.65.$4.10Current price is: $4.10.
-38%
Bestsellers
eText ISBN: 9781423207788
$6.65 Original price was: $6.65.$4.10Current price is: $4.10.
Reviews
There are no reviews yet.