A Note on Mathematical Modelling of Practical Multicampaign Assignment and Its Computational Complexity

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Kim, Yong-Hyuk
Yoon, Yourim
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Abstract
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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.
Comment: 14 pages, 1 figure
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Computer Science - Computational Complexity, Computer Science - Other Computer Science
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