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This is actually pretty straightforward to overcome (and one of the real strengths of the bayesian approach). Rather than using the direct counts of success for each group, add a prior belief that americans will favor baseball and non-americans will favor soccer (you can experiment to determine this number).

You can now evaluate the results conditioned on each group (american / non-american).




Or if you don't want to add in a prior specific belief about americans vs. non-americans, just keep one counter per option per continent-of-source-IP (or whatever) and the learning algorithm should work it out on its own. Of course, if you use too many bins then learning is going to take far too long.




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