White Papers

Learning to Rank With Nonsmooth Cost Functions

Overview 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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Publisher
Microsoft
File Format
PDF
Date Published
Jul 19, 2007
Format
White Papers
Topics
Software Engineering

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