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I have gone through few articles but I am not convinced on what should I do with these. I know from business standpoint it might be good to consider fraudulent transactions happening from unknown locations. But I don't know how to use this in my data as dummy encoding might not be good solution.

how to represent geography or zip code in machine learning model or recommender system?

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Why do you think that

... as dummy encoding might not be good solution

? If it is because of memory issues, find some software that is using sparse matrices, like glmnet. Since there are very many different zip codes, have a look at Principled way of collapsing categorical variables with many levels? and the suggestions there. I would try out the fused lasso.

It is also possible that for your application, some domain knowledge would be helpful for feature construction.

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