Large-scale Linear RankSVM

• Main Authors: Ching-Pei Lee, 李靜沛
• Other Authors: Chih-Jen Lin
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/95333963386449972611

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 101 === Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following the recent development of linear SVM for classi cation, linear rankSVM may give competitive performance for large and sparse data. Many existing works have studied linear rankSVM. Their focus is on the computational e ciency when the number of preference pairs is large. In this thesis, we systematically study past works, discuss their advantages/disadvantages, and propose an e cient algorithm. Di erent implementation issues and extensions are discussed with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.