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
Author(s): Hang Li
Series: Synthesis Lectures on Human Language Technologies
Edition: 2
Publisher: Morgan & Claypool
Year: 2015
Language: English
Pages: xiv+107
Preface
Learning to Rank
Ranking
Learning to Rank
Ranking Creation
Ranking Aggregation
Learning for Ranking Creation
Learning for Ranking Aggregation
Learning for Ranking Creation
Document Retrieval as Example
Learning Task
Training and Testing
Training Data Creation
Feature Construction
Evaluation
Relations with Other Learning Tasks
Learning Approaches
Pointwise Approach
Pairwise Approach
Listwise Approach
Evaluation Results
Learning for Ranking Aggregation
Learning Task
Learning Methods
Methods of Learning to Rank
PRank
Model
Learning Algorithm
OC SVM
Model
Learning Algorithm
McRank
Model
Learning Algorithm
Ranking SVM
Linear Model as Ranking Function
Ranking SVM Model
Learning Algorithm
IR SVM
Modified Loss Function
Learning Algorithm
GBRank
Loss Function
Learning Algorithm
RankNet
Loss Function
Model
Learning Algorithm
Speed up of Training
ListNet and ListMLE
Plackett-Luce model
ListNet
ListMLE
AdaRank
Loss Function
Learning Algorithm
SVM MAP
Loss Function
Learning Algorithms
SoftRank
Soft NDCG
Approximation of Rank Distribution
Learning Algorithm
LambdaRank
Loss Function
Learning Algorithm
LambdaMART
Model and Loss Function
Learning Algorithm
Borda Count
Markov Chain
Cranking
Model
Learning Algorithm
Prediction
Applications of Learning to Rank
Theory of Learning to Rank
Statistical Learning Formulation
Loss Functions
Relations between Loss Functions
Theoretical Analysis
Ongoing and Future Work
Bibliography
Author's Biography
