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
