Titlebook: Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition; Hang Li Book 2015Latest edition Springer Natur

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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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Methods of Learning to Rank,R SVM [14], GBRank [125,126], RankNet [12], ListNet & ListMLE [15, 115], AdaRank [119], SVM MAP [122], SoftRank [45, 102], LambdaRank [13, 34], and LambdaMART [11, 113], and three methods for ranking aggregation, including Borda Count [36], Markov Chain [36], and CRanking [66].

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Applications of Learning to Rank,ocument retrieval, expert search, definition search, meta-search, personalized search, online advertisement, collaborative filtering, question answering, keyphrase extraction, document summarization, and machine translation.

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Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition978-3-031-02155-8Series ISSN 1947-4040 Series E-ISSN 1947-4059

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