Optimizing Display Advertising in Online Social Networks

Advertising is a significant source of revenue for most online social networks. Conventional online advertising methods need to be customized for online social networks in order to address their distinct characteristics. Recent experimental studies have shown that providing social cues along with ads, e.g. information about friends liking the ad or clicking on an ad, leads to higher click rates. In other words, the probability of a user clicking an ad is a function of the set of friends that have clicked the ad. In this work, we propose formal probabilistic models to capture this phenomenon, and study the algorithmic problem that then arises. Our work is in the context of display advertising where a contract is signed to show an ad to a pre-determined number of users. The problem we study is the following: given a certain number of impressions, what is the optimal display strategy, i.e. the optimal order and the subset of users to show the ad to, so as to maximize the expected number of clicks? Unlike previous models of influence maximization, we show that this optimization problem is hard to approximate in general, and that it is related to finding dense subgraphs of a given size. In light of the hardness result, we propose several heuristic algorithms including a two-stage algorithm inspired by influence-and-exploit strategies in viral marketing. We evaluate the performance of these heuristics on real data sets, and observe that our two-stage heuristic significantly outperforms the natural baselines.