Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising

TL;DR

This paper introduces a novel ad delivery policy that minimizes user traffic consumption in guaranteed advertising, enabling more contracts and revenue while being robust and efficient compared to traditional methods.

Contribution

The paper proposes a near-optimal delivery method focused on minimizing user traffic usage, a new approach differing from existing models that prioritize penalty reduction.

Findings

Our method consumes nearly the minimal user traffic needed.

It allows better estimation of redundant or short traffic for trading.

Simulation results show it outperforms traditional approaches.

Abstract

In this work, we study the guaranteed delivery model which is widely used in online display advertising. In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers. A crucial problem for the advertising platform is how to fully utilize the valuable user traffic to generate as much as possible revenue. Different from previous works which usually minimize the penalty of unsatisfied contracts and some other cost (e.g. representativeness), we propose the novel consumption minimization model, in which the primary objective is to minimize the user traffic consumed to satisfy all contracts. Under this model, we develop a near optimal method to deliver ads for users. The main advantage of our method lies in that it consumes nearly as least as possible user traffic to satisfy all contracts, therefore more contracts can be accepted to produce more revenue. It also enables the publishers to estimate how much user traffic is redundant or short so that they can sell or buy this part of traffic in bulk in the exchange market. Furthermore, it is robust with regard to priori knowledge of user type distribution. Finally, the simulation shows that our method outperforms the traditional state-of-the-art methods.