Title: Ads that Stick: Near-Optimal Ad Optimization through Psychological Behavior Models
Primary area: learning on time series and dynamical systems
Keywords: Mathematical User Modelling, Psychological Model, Ad Optimization, Scheduling, Digital Advertising, Behavioral Modeling, Satiation Effect

Abstract: Optimizing the timing and frequency of advertisements (ads) is a central problem in digital advertising, with significant economic consequences. Existing scheduling policies rely on simple heuristics, such as uniform spacing and frequency caps, that overlook long-term user interest. However, it is well-known that users' long-term interest and engagement result from the interplay of several psychological effects (Curmei, Haupt, Recht, and Hadfield-Menell, ACM CRS, 2022).

In this work, we model change in user interest upon showing ads based on three key psychological principles: mere exposure, hedonic adaptation, and operant conditioning. The first two effects are modeled using a concave function of user interest with repeated exposure, while the third effect is modeled using a temporal decay function, which explains the decline in user interest due to overexposure.
Under our psychological behavior model, we ask the following question: Given a continuous time interval $T$, how many ads should be shown, and at what times, to maximize the user interest towards the ads?

Towards answering this question, we first show that, if the number of displayed ads is fixed to $n$, then the optimal ad-schedule only depends on the operant conditioning function. Our main result is a quasi-linear time algorithm that, given the number of ads $n$, outputs a near-optimal ad-schedule, i.e., the difference in the performance of our schedule and the optimal schedule is exponentially small.
Our algorithm leads to significant insights about optimal ad placement and shows that simple heuristics such as uniform spacing are sub-optimal under many natural settings. The optimal number of ads to display, which also depends on the mere exposure and hedonistic adaptation functions, can be found through a simple linear search given the above algorithm. We further support our findings with experimental results, demonstrating that our strategy outperforms various baselines.

TL;DR: We model ad scheduling with psychological effects and design a provably near-optimal algorithm that improves over standard heuristics.