书目名称 | Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition | 编辑 | Hang Li | 视频video | | 丛书名称 | Synthesis Lectures on Human Language Technologies | 图书封面 |  | 描述 | Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation an | 出版日期 | Book 2015Latest edition | 版次 | 2 | doi | https://doi.org/10.1007/978-3-031-02155-8 | isbn_softcover | 978-3-031-01027-9 | isbn_ebook | 978-3-031-02155-8Series ISSN 1947-4040 Series E-ISSN 1947-4059 | issn_series | 1947-4040 | copyright | Springer Nature Switzerland AG 2015 |
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书目名称Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition被引频次 
书目名称Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition被引频次学科排名 
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