Optimal Ad-Ranking for Profit Maximization

Despite the enormous commercial importance of on-line advertisements (ads), there has been little work done to clarify the basis for ranking and displaying them. Most existing methods rank ads as if the user views each of them in isolation. We will consider a more realistic user model that induces three mutual influences between displayed ads: (i) positional bias (for viewing ads placed higher up) (ii) similar ad fatigue (which reduces interest in an ad when similar ads have already been displayed above it) and (iii) browsing impatience (which accounts for the user abandoning the ad viewing based on the ads already seen). We will show that in general, when the inter-ad similarity is taken into account, optimal ranking is NP-hard. Ignoring inter-ad similarity, we state and prove the optimal ranking function for sorting the ads that is sensitive to the other two factors. We will show that the known ad ranking strategies correspond to restricted special cases of our ranking function. We also provide simulation studies that establish the effectiveness of its generality.