New Online Algorithms for Story Scheduling in Web Advertising

Abstract

We study storyboarding where advertisers wish to present sequences of ads (stories) uninterruptedly on a major ad position of a web page. These jobs/stories arrive online and are triggered by the browsing history of a user who at any time continues surfing with probability β. The goal of an ad server is to construct a schedule maximizing the expected reward. The problem was introduced by Dasgupta, Ghosh, Nazerzadeh and Raghavan (SODA’09) who presented a 7-competitive online algorithm. They also showed that no deterministic online strategy can achieve a competitiveness smaller than 2, for general β.

We present improved algorithms for storyboarding. First we give a simple online strategy that achieves a competitive ratio of 4/(2 − β), which is upper bounded by 4 for any β. The algorithm is also 1/(1 − β)-competitive, which gives better bounds for small β. As the main result of this paper we devise a refined algorithm that attains a competitive ratio of c = 1 + φ, where $\phi=(1+\sqrt{5})/2$ is the Golden Ratio. This performance guarantee of c ≈ 2.618 is close to the lower bound of 2. Additionally, we study for the first time a problem extension where stories may be presented simultaneously on several ad positions of a web page. For this parallel setting we provide an algorithm whose competitive ratio is upper bounded by $1/(3-2\sqrt{2})\approx 5.828$, for any β. All our algorithms work in phases and have to make scheduling decisions only every once in a while.