Learning to Rank with Non-Smooth Cost Functions
Advances in Neural Information Processing Systems 19 |
Published by MIT Press, Cambridge, MA
The quality measures used in information retrieval are particularly difﬁcult to optimize directly, since they depend on the model scores only through the sorted order of the documents returned for a given query. Thus, the derivatives of the cost with respect to the model parameters are either zero, or are undeﬁned. In this paper, we propose a class of simple, ﬂexible algorithms, called LambdaRank, which avoids these difﬁculties by working with implicit cost functions. We describe LambdaRank using neural network models, although the idea applies to any differentiable function class. We give necessary and sufﬁcient conditions for the resulting implicit cost function to be convex, and we show that the general method has a simple mechanical interpretation. We demonstrate signiﬁcantly improved accuracy, over a state-of-the-art ranking algorithm, on several datasets. We also show that LambdaRank provides a method for signiﬁcantly speeding up the training phase of that ranking algorithm. Although this paper is directed towards ranking, the proposed method can be extended to any non-smooth and multivariate cost functions.