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Identification of Seasonality in Internet Traffic to Support Control of Online Advertising | IEEE Conference Publication | IEEE Xplore

Identification of Seasonality in Internet Traffic to Support Control of Online Advertising


Abstract:

Feedback control is widely applied to the campaign management in online advertising. Learning the pattern of user traffic on Internet plays an important role in solving t...Show More

Abstract:

Feedback control is widely applied to the campaign management in online advertising. Learning the pattern of user traffic on Internet plays an important role in solving the control problem. In this paper, we focus on characterizing the seasonality, e.g., time of day(TOD) pattern of Internet user traffic for individual ad campaign. We model the seasonality using a truncated Fourier series with a set of amplitude and phase parameters. These seasonality parameters are estimated in a Bayesian framework using a minimum mean square error(MMSE) estimator, with their prior distribution learnt from historical data of a large number of campaigns. The proposed Bayesian method is shown to be robust and renders sensible seasonality for campaigns of disparate noise levels.
Date of Conference: 10-12 July 2019
Date Added to IEEE Xplore: 29 August 2019
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Conference Location: Philadelphia, PA, USA

I. Introduction

Online advertising is a fast growing industry and in order to deliver the campaign budget smoothly over time, as desired by the advertiser, it is critical to implement feedback control in the campaign management system. An early paper on feedback control applied to online advertising is available in [1], wherein several important challenges are outlined but detailed solutions are omitted. A more comprehensive and up-to-date overview of the control problem is available in [2]. The fact that the plant is unknown, dynamic, periodic, nonlinear, and in general discontinuous is a characteristic property of online advertising processes and is a fundamental challenge in the development of feedback control solutions. One of the main issues is that the user traffic to different web sites is volatile with stochastic effects as well as with trends and seasonality. In this paper, the Internet user traffic is represented by the number of impressions available during a fixed period of time, where an impression is one view of an ad. The seasonality, in particular, is dramatic and unless it is carefully accounted for during the feedback control, the advertiser may end up paying an unnecessary high price for impressions during hours of the day when the available number of impressions is low.

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