Extracting Actionable Knowledge from Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Dynamic Catalog Mailing Policies
Management Science
Assigning discounts in a marketing campaign by using reinforcement learning and neural networks
Expert Systems with Applications: An International Journal
Supporting smart interactions with predictive analytics
The smart internet
Supporting smart interactions with predictive analytics
The smart internet
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We empirically evaluate the performance of various re-inforcementlearning methods in applications to sequentialtargeted marketing. In particular, we propose and evaluatea progression of reinforcement learning methods, rangingfrom the "direct" or "batch" methods to "indirect" or"simulation based" methods, and those that we call "semi-direct"methods that fall between them. We conduct a num-berof controlled experiments to evaluate the performanceof these competing methods. Our results indicate that whilethe indirect methods can perform better in a situation inwhich nearly perfect modeling is possible, under the morerealistic situations in which the system's modeling parametershave restricted attention, the indirect methods' performancetend to degrade. We also show that semi-directmethods are effective in reducing the amount of computationnecessary to attain a given level of performance, andoften result in more profitable policies.