  Within personalized marketing, a recommendation issue known as multicampaign
assignment is to overcome a critical problem, known as the multiple
recommendation problem which occurs when running several personalized campaigns
simultaneously. This paper mainly deals with the hardness of multicampaign
assignment, which is treated as a very challenging problem in marketing. The
objective in this problem is to find a customer-campaign matrix which maximizes
the effectiveness of multiple campaigns under some constraints. We present a
realistic response suppression function, which is designed to be more
practical, and explain how this can be learned from historical data. Moreover,
we provide a proof that this more realistic version of the problem is NP-hard,
thus justifying to use of heuristics presented in previous work.
