A machine learning approach to learning to rank trains a

model to optimize a target evaluation measure with repect

to training data. Currently, existing information retrieval

measures are impossible to optimize directly except for models

with a very small number of parameters. The IR community

thus faces a major challenge: how to optimize IR

measures of interest directly. In this paper, we present a

solution. Specifically, we show that LambdaRank [1], which

smoothly approximates the gradient of the target measure,

can be adapted to work with four popular IR target evaluation

measures using the same underlying gradient construction.

It is likely, therefore, that this construction is

extendable to other evaluation measures. We empirically

show that LambdaRank finds a locally optimal solution for

mean NDCG@10, mean NDCG, MAP and MRR with a 99%

confidence rate. We also show that the amount of effective

training data varies with IR measure and that with a sufficiently

large training set size, matching the training optimization

measure to the target evaluation measure yields

the best accuracy.

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In: SIGIR

Publisher: Association for Computing Machinery, Inc.
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