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I am asking if there already exist approaches and researches on the following topic.

Imagine there are 10 stores and in 3 stores labeled training data was available, so I built 3 classification models based on those datasets (e.g. to classify my customers into A, B, C customers).

All stores basically have similar characteristics, but Model A will work best with Store A, Model B best with Store B, etc. because of geographical or cultural differences.

If training data is not available for my other 7 stores, is there any approach to find out if I should use Model A, B or C for my Store D data?

I assume the data distribution of Store D must be compared with those of A, B and C to estimate which model will work best. Any ideas or recommendations of existing papers about that topic?

Thanks!

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  • $\begingroup$ Isn't a single model that accounts for the differences between stores possible? From your short description it sounds like it is not about individual stores but rather the differences between them. $\endgroup$
    – Tim
    Commented Feb 7, 2015 at 18:10
  • $\begingroup$ I agree with @Tim, a single model should be able to handle this $\endgroup$
    – bdeonovic
    Commented Feb 7, 2015 at 18:14
  • $\begingroup$ My question is, given that store A is in USA, store B in Australia and store C in Russia and each model works best in its country because there are country-specific characteristics in the data. Now store D opens in Italy and all you have is unlabeled data, so you can't train a new model for store D and you can't validate your existing models because you don't know the labels. Is there an approach to decide which model (A, B or C) works best for store D by comparing the data distributions from store D to stores A, B and C? $\endgroup$
    – HansHupe
    Commented Feb 8, 2015 at 10:11

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Please check the paper "A survey on Transfer Learning" by SJ Pan. Your problem is related to domain adaptation and I think the survey will help you to find related methods to your problem. (I see that the reply is a bit too late though :))

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