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Titlebook: Learning to Rank for Information Retrieval and Natural Language Processing; Hang Li Book 2011 Springer Nature Switzerland AG 2011

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發(fā)表于 2025-3-21 18:43:23 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Learning to Rank for Information Retrieval and Natural Language Processing
編輯Hang Li
視頻videohttp://file.papertrans.cn/584/583011/583011.mp4
叢書(shū)名稱(chēng)Synthesis Lectures on Human Language Technologies
圖書(shū)封面Titlebook: Learning to Rank for Information Retrieval and Natural Language Processing; Hang Li Book 2011 Springer Nature Switzerland AG 2011
描述Learning to rank refers to machine learning techniques for training the 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 the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing 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
出版日期Book 2011
版次1
doihttps://doi.org/10.1007/978-3-031-02141-1
isbn_ebook978-3-031-02141-1Series ISSN 1947-4040 Series E-ISSN 1947-4059
issn_series 1947-4040
copyrightSpringer Nature Switzerland AG 2011
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發(fā)表于 2025-3-22 00:11:06 | 只看該作者
Learning to Rank for Information Retrieval and Natural Language Processing978-3-031-02141-1Series ISSN 1947-4040 Series E-ISSN 1947-4059
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發(fā)表于 2025-3-22 04:47:34 | 只看該作者
Learning for Ranking Aggregation,le ranking, which is better than any of the original rankings in terms of an evaluation measure. Learning for ranking aggregation is about building a ranking model for ranking aggregation using machine learning techniques.
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發(fā)表于 2025-3-22 12:31:26 | 只看該作者
Methods of Learning to Rank,ank [114, 115], RankNet [11], LambdaRank [12, 32], ListNet & ListMLE [14, 104], AdaRank [108], SVM MAP [111], and SoftRank [43, 95], and three methods for ranking aggregation, including Borda Count [34], Markov Chain [34], and CRanking [63].
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發(fā)表于 2025-3-22 14:00:09 | 只看該作者
Applications of Learning to Rank,ocument retrieval, expert search, definition search, meta-search, personalized search, online advertisement, collaborative filtering, question answering, key phrase extraction, document summarization, and machine translation.
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Synthesis Lectures on Human Language Technologieshttp://image.papertrans.cn/l/image/583011.jpg
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