Author: Tie-Yan Liu
Edition: 2011
Publisher: Springer
Binding: Hardcover
ISBN: 3642142664
Category: Programming
List Price: $ 99.00
Price: $ 83.33
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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. Programming books Learning to Rank for Information Retrieval pdf. 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”. Download books Learning to Rank for Information Retrieval pdf via mediafire, 4shared, rapidshare.
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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 coll
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 coll
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Learning to Rank for Information Retrieval: Tie-Yan Liu
Learning to Rank for Information Retrieval: Tie-Yan Liu
Price: $3.9
From Publishers Weekly Starred Review. At one time a fringe notion, the idea of geoengineering-using radical means to change the climate deliberately-is gaining traction in scientific conferences and even in the White House, where doubts are growing regarding the efficacy of mainstream strategies (conservation, alternative energy, "storing carbon dioxide from coal plants in the ground"). In this fascinating wake-up call, Science magazine writer Kintisch begins with the startling notion that "cl
From Publishers Weekly Starred Review. At one time a fringe notion, the idea of geoengineering-using radical means to change the climate deliberately-is gaining traction in scientific conferences and even in the White House, where doubts are growing regarding the efficacy of mainstream strategies (conservation, alternative energy, "storing carbon dioxide from coal plants in the ground"). In this fascinating wake-up call, Science magazine writer Kintisch begins with the startling notion that "cl
Price: $99
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 coll
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 coll
Download Learning to Rank for Information Retrieval
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. Download free Learning to Rank for Information Retrieval pdf
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