| Summary: | 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.
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