Maximum Margin Ranking Algorithms for Information Retrieval

Machine learning ranking methods are increasingly applied to ranking tasks in information retrieval (IR). However ranking tasks in IR often differ 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-theart 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 resulting optimization problems. Two of these are stochastic gradient projection algorithms, one of which relies on a recent method for l1,∞-norm projections; the third is a stochastic exponentiated gradient algorithm. The algorithms are simple and efficient, have provable convergence properties, and in our preliminary experiments, show performance close to state-of-the-art algorithms that directly optimize IR ranking measures.