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. This paper proposes a class of simple, flexible algorithms, called LambdaRank, which avoids these difficulties by working with implicit cost functions. The paper describes LambdaRank using neural network models, although the idea applies to any differentiable function class.
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