Learning to Rank for Information Retrieval and Natural Language Processing

Description

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 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 LambdaRank Loss Function Learning Algorithm 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 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