Ranking SVM on LETOR

Introduction

Introduction to Ranking SVM 

The basic idea of Ranking SVM is to formalize learning to rank as a problem of
binary classification on instance pairs, and then to solve the problem using Support Vector Machines. The details of Ranking SVM can be found from http://www.research.microsoft.com/~rherb/papers/herobergrae99.ps.gz and http://svmlight.joachims.org/.


Learning Parameters 

We used a code based on primal optimization of the objective function as described in this paper. The multiplicator in front of the loss term is C/n where n is the number of preference pairs and C is chosen on the validation set in the set


Papers & Docs 

R. Herbrich, T. Graepel, and K. Obermayer. Large Margin Rank Boundaries for Ordinal Regression. Advances in Large Margin Classifiers, 115132, Liu Press, 2000. T. Joachims. Optimizing Search Engines Using Clickthrough Data, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2002. O. Chapelle and S. S. Keerthi. Efficient algorithms for ranking with SVMs. Information Retrieval Journal, Special Issue on Learning to Rank, 2009. to appear.
BibTex
@inproceedings{775067,


Notes 

This document was written by Olivier Chapelle, and the experiments were
conducted by Olivier Chapelle. If any problem, please contact letor@microsoft.com or chap@yahooinc.com
