Yield Optimization with Binding Latency Constraints

Abstract

Programmatic advertising is an actively developing industry and research area. Some of the research in this area concerns the development of optimal or approximately optimal contracts and policies between publishers, advertisers and intermediaries such as ad networks and ad exchanges. Both the development of contracts and the construction of policies governing their implementation are difficult challenges, and different models take different features of the problem into account. In programmatic advertising decisions are made in real time, and time is a scarce resource particularly for publishers who are concerned with content load times. Policies for advertisement placement must execute very quickly once content is requested, this requires policies to either be pre-computed and accessed as needed, or for the policy execution to be very efficient. In this paper we formulate a stochastic optimization problem for per publisher ad sequencing with binding latency constraints. We adopt a well known heuristic optimization technique to this problem and evaluate it's performance on real data instances. Our experimental results indicate that our heuristic algorithm is near optimal for instances where an optimality calculation is feasible, and superior to other reasonable approaches for instances when the calculation is not feasible.