| 书目名称 | Learning to Rank for Information Retrieval | | 编辑 | Tie-Yan Liu | | 视频video | | | 概述 | Only comprehensive overview of a key innovative technology for search engine development.Written by one of the leading authorities in this field.Combines scientific theoretical soundness with broad de | | 图书封面 |  | | 描述 | .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 t | | 出版日期 | Book 20111st edition | | 关键词 | Information Retrieval; Machine Learning; Ranking Algorithms; Statistical Learning | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-642-14267-3 | | isbn_softcover | 978-3-642-44124-0 | | isbn_ebook | 978-3-642-14267-3 | | copyright | Springer-Verlag Berlin Heidelberg 2011 |
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发表于 2025-3-21 17:02:34
