Stochastic dynamic programming model for revenue optimization in social networks

The rapid increase in the global availability and use of the Internet has made it the most effective platform for the distribution of product information and advertising when compared to the traditional media such as print, television and radio. In addition, in the case of Online Social Networks (OSN), one can take advantage of the influences that one´s actions has on other users and, in particular, one´s friends. Even if a user does not purchase a product, their online mention of the product can have positive or negative influences. This social behavior can be used to improve advertising strategies. For example, if a user is given an advertisement impression (a linked icon) and clicks on it then it is more likely that their friends (if told of the action) will also click if they are subsequently given the impression. In this paper we provide a Stochastic Dynamic Programming formulation of this problem together with its solution. Because of the computational complexity of the solution we provide a simple heuristic that we show to be computationally much faster.