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

Intervention

Hang Liocial identities. More precisely, it is about two distinctively different and yet interrelated processes: the construction and presentation of identities. It also explores the intersections of identity and community within various event contexts. The book’s interdisciplinary approach, cross-cultural

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G-spot

Learning for Ranking Creation, the features of the offerings and the request, so that ‘good’ offerings to the request are ranked at the top. Learning for ranking creation is concerned with automatic construction of the ranking model using machine learning techniques.

TRACE

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.

lambaste

老人病學

細菌等

Theory of Learning to Rank,This chapter gives a statistical learning formulation of learning to rank (ranking creation) and explains the issues in theoretical study of learning to rank.

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