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I am fairly new in the field of Information retrieval. I have basic knowledge about machine learning. I understand the purpose of CV in the context of Machine learning. However, I've become a bit confused when I saw CV used in the context of Information retrieval.

Here, in this paper the authors said: "values of the free parameters are set using leave-one-out cross validation performed over queries, where MAP serves as the optimization criterion."

How to perform CV over queries?

Here is what I am thinking, we should split the queries (in the test collection) into 10-folds,

For i in 10:

  1. Using the training 90% part, we optimize the free parameter p (whatever the parameter is) for MAP (chose p that yield to the best MAP over queries)
  2. Test the chosen K against the testing part.

The Question is: After 10 iterations, we end up with 10 different values of P, what value should I use?

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A very typical method to set parameters of a model is through maximum likelihood estimation; i.e., set the parameters to values that maximizes the likelihood of the observed data.

I presume that when the authors say they set the parameters of the model through cross-validation, they chose the values of the parameters (or, more likely given that the discuss using MAP estimation, the hyper-parameters) that the minimizes the estimated out-of-sample error during cross-validation, for some given loss function.

So I would guess that the authors are using cross-validation to the pick the hyper-parameters and then fitting the full data using those hyper parameters selected by cross-validation.

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