Ads that Stick: Near-Optimal Ad Optimization through Psychological Behavior Models

2025-09-24

AI Summary

This paper addresses the neglect of users’ long-term interest dynamics—rooted in cognitive psychological mechanisms—in digital ad scheduling. Methodologically, it introduces the first unified model integrating the mere-exposure effect, hedonic adaptation, and operant conditioning, employing a concave interest decay function; it proves that the optimal schedule depends solely on the operant conditioning function and proposes a quasilinear-time algorithm achieving near-optimal scheduling over an exponential solution space. Theoretically, it exposes the intrinsic limitations of heuristic strategies (e.g., uniform spacing) and establishes the first ad scheduling framework jointly incorporating these three core psychological mechanisms. Empirical evaluation on real-world data demonstrates significant improvements in users’ long-term interest retention and engagement metrics, consistently outperforming multiple baseline scheduling policies.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Adversarial Search
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Optimizing the timing and frequency of 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, 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, then the optimal ad-schedule only depends on the operant conditioning function. Our main result is a quasi-linear time algorithm that 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.

Problem

• Optimizing ad timing and frequency using psychological behavior models
• Modeling user interest changes through exposure and conditioning effects
• Developing algorithms for near-optimal ad scheduling to maximize engagement

Innovation

• Modeled user interest using three psychological principles
• Developed quasi-linear time near-optimal scheduling algorithm
• Optimized ad timing and frequency through behavioral functions