In the case where you know a user did not like an item, how should the information be treated in a recommender? Normally for retail recommendations you have an implicit 1 for a purchase and no value otherwise. But what if you knew the user did not like an item? Maybe you have records of "I want my money back for this junk" reactions.
You could make a scale, 0, 1 where 0 means a bad rating and 1 a good, no value as usual means no preference? Some of the math here won't work though since usually no value implicitly = 0 so maybe -1 = bad, 1 = good, no preference implicitly = 0? Would it be better to treat the bad rating as a 1 and good as 2? This would be more like the old star rating method only we would know where the cutoff should be between a good review and bad (1.5) I suppose this could also be treated as another recommender in an ensemble where r = r_p - r_h, where r_h = predictions from "I hate this product" preferences? Has anyone found a good method?
