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 and ranking aggregation, including
training and testing, evaluation, feature creation, and major
approaches. Many methods have been proposed for ranking creation.
The methods can be categorized as the pointwise, pairwise, and
listwise approaches according to the loss functions they employ.
They can also be categorized according to the techniques they
employ, such as the SVM based, Boosting based, and Neural Network
based approaches. The author also introduces some popular learning
to rank methods in details. These include: PRank, OC SVM, McRank,
Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE,
AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count,
Markov Chain, and CRanking. The author explains several example
applications of learning to rank including web search,
collaborative filtering, definition search, keyphrase extraction,
query dependent summarization, and re-ranking in machine
translation. A formulation of learning for ranking creation is
given in the statistical learning framework. Ongoing and future
research directions for learning to rank are also discussed. Table
of Contents: Learning to Rank / Learning for Ranking Creation /
Learning for Ranking Aggregation / Methods of Learning to Rank /
Applications of Learning to Rank / Theory of Learning to Rank /
Ongoing and Future Work
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