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Maximum Margin Ranking Algorithms for Information Retrieval
 

Summary: Maximum Margin Ranking Algorithms for
Information Retrieval
Shivani Agarwal and Michael Collins
Massachusetts Institute of Technology, Cambridge MA 02139, USA
{shivani,mcollins}@csail.mit.edu
Abstract. Machine learning ranking methods are increasingly applied to rank-
ing tasks in information retrieval (IR). However ranking tasks in IR often dif-
fer from standard ranking tasks in machine learning, both in terms of problem
structure and in terms of the evaluation criteria used to measure performance.
Consequently, there has been much interest in recent years in developing ranking
algorithms that directly optimize IR ranking measures. Here we propose a family
of ranking algorithms that preserve the simplicity of standard pair-wise ranking
methods in machine learning, yet show performance comparable to state-of-the-
art IR ranking algorithms. Our algorithms optimize variations of the hinge loss
used in support vector machines (SVMs); we discuss three variations, and in each
case, give simple and efficient stochastic gradient algorithms to solve the result-
ing optimization problems. Two of these are stochastic gradient projection algo-
rithms, one of which relies on a recent method for l1,-norm projections; the
third is a stochastic exponentiated gradient algorithm. The algorithms are sim-
ple and efficient, have provable convergence properties, and in our preliminary

  

Source: Agarwal, Shivani - Department of Computer Science and Automation, Indian Institute of Science, Bangalore

 

Collections: Computer Technologies and Information Sciences