Learning to Rank for Information Retrieval by Tie-Yan Liu (Author)

Learning to Rank for Information Retrieval by Tie-Yan Liu (Author)
Item# 13020640193
Retail price: US$99.00
Sale price: US$8.00

all items in this store are to be sent to your email within 24 hours after cleared payment. PDF eBooks are sent to you as email attachments. as for mp3 audiobook, a download link from ONEDRIVE will be sent to your email for you to download.

Please Read Before Your Purchase!!!

1. This item is an E-Book in PDF format.

2. Shipping & Delivery: Send to you by E-mail within 24 Hours after cleared payment. Immediately Arrival!!!

3. Shipping ( by email) + Handling Fee = US$0.00

4. Time-Limited Offer, Order Fast.

*************************************************************************









Learning to Rank for Information Retrieval by Tie-Yan Liu (Author)



Publisher: Springer; 2011 edition (May 6, 2011)

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.

Review From the reviews: “The book treats a very hot research topic: that of ranking great amounts of documents based on their relation to a given query, i.e., the examination of the inner mechanics of the search engines. The text is especially addressed to information retrieval and machine learning specialists and graduate students, but it might appeal to scientists from other related fields, too.” (Ruxandra Stoean, Zentralblatt MATH, Vol. 1227 2012) From the Back Cover 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.