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Heuristics for advertising revenue optimization in Online Social Networks

Recent increases in the adoption of Online Social Networks (OSNs) for advertising has resulted in the research and development of algorithms that can maximize the resulting revenue. OSN users are likely to be influenced by their friends; therefore, one can leverage friendship relationships to determine how advertisements should be distributed among users. If a user is given an indication that their friend clicked on an advertisement link (called an impression), then they are more likely to also click on the impression if it were to be provided to them. The problem of assigning impressions can be modeled as an optimization problem in which the goal is to maximize the expected number of clicks achieved given a fixed number of impressions. Hosein and Lawrence [1] formulated this as a Stochastic Dynamic Programming problem in which impressions are provided in stages and the outcomes of previous stages are used in making impression allocations for the present stage. However, the determination of the optimal solution is computationally intractable for large problems; hence we require heuristics that are efficient while providing near-optimal solutions. In this paper we provide and compare various heuristics for this problem.