User Interaction with Online Advertisements: Temporal Modeling and Optimization of Ads Placement

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• Contributors: Luca Vassio, Michele Garetto, Carla Chiasserini, Emilio Leonardi
• Year: 2020
• Venue: ACM Transactions on Modeling and Performance Evaluation of Computing Systems
• Abstract:

  We consider an online advertisement system and focus on the impact of user interaction and response to targeted advertising campaigns. We analytically model the system dynamics accounting for the user behavior and devise strategies to maximize a relevant metric called click-through-intensity (CTI), defined as the number of clicks per time unit. With respect to the traditional click-through-rate (CTR) metric, CTI better captures the success of advertisements for services that the users may access several times, making multiple purchases or subscriptions. Examples include advertising of on-line games or airplane tickets. The model we develop is validated through traces of real advertising systems and allows us to optimize CTI under different scenarios depending on the nature of ad delivery and of the information available at the system. Experimental results show that our approach can increase the revenue of an ad campaign, even when user’s behavior can only be estimated.

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