Should I send this notification? Optimizing push notifications decision making by modeling the future
Conor O'Brien, Huasen Wu, Shaodan Zhai, Dalin Guo, Wenzhe Shi,, Jonathan J Hunt

TL;DR
This paper presents a model-based reinforcement learning approach to optimize long-term user satisfaction by intelligently deciding when to send push notifications, outperforming heuristic methods in a large social network.
Contribution
It introduces a reinforcement learning framework for long-term push notification optimization, extending beyond session-based methods to multi-day horizons.
Findings
Sent fewer notifications while maintaining engagement.
Achieved higher open rates compared to baseline.
Demonstrated effectiveness in a real-world A/B test.
Abstract
Most recommender systems are myopic, that is they optimize based on the immediate response of the user. This may be misaligned with the true objective, such as creating long term user satisfaction. In this work we focus on mobile push notifications, where the long term effects of recommender system decisions can be particularly strong. For example, sending too many or irrelevant notifications may annoy a user and cause them to disable notifications. However, a myopic system will always choose to send a notification since negative effects occur in the future. This is typically mitigated using heuristics. However, heuristics can be hard to reason about or improve, require retuning each time the system is changed, and may be suboptimal. To counter these drawbacks, there is significant interest in recommender systems that optimize directly for long-term value (LTV). Here, we describe a…
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Taxonomy
TopicsPersonal Information Management and User Behavior · Opportunistic and Delay-Tolerant Networks · Caching and Content Delivery
