McRank: Learning to Rank Using Multiple Classification and Gradient Boosting

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Ping Li, Qiang Wu, Christopher Burges


We cast the ranking problem as (1) multiple classification (“Mc”) (2) multiple or- dinal classification, which lead to computationally tractable learning algorithms for relevance ranking in Web search. We consider the DCG criterion (discounted cumulative gain), a standard quality measure in information retrieval. Our ap- proach is motivated by the fact that perfect classifications result in perfect DCG scores and the DCG errors are bounded by classification errors. We propose us- ing the Expected Relevance to convert class probabilities into ranking scores. The class probabilities are learned using a gradient boosting tree algorithm. Evalua- tions on large-scale datasets show that our approach can improve LambdaRank [5] and the regressions-based ranker [6], in terms of the (normalized) DCG scores. An efficient implementation of the boosting tree algorithm is also presented.