Multi-objective Optimization of Notifications Using Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2207.03029v1
• Date: Thu, 7 Jul 2022 00:53:08 GMT
• Title: Multi-objective Optimization of Notifications Using Offline Reinforcement Learning
• Authors: Prakruthi Prabhakar, Yiping Yuan, Guangyu Yang, Wensheng Sun, Ajith Muralidharan
• Abstract summary: We formulate the near-real-time notification decision problem as a Markov Decision Process. We propose an end-to-end offline reinforcement learning framework to optimize sequential notification decisions.
• Score: 1.2303635283131926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile notification systems play a major role in a variety of applications to communicate, send alerts and reminders to the users to inform them about news, events or messages. In this paper, we formulate the near-real-time notification decision problem as a Markov Decision Process where we optimize for multiple objectives in the rewards. We propose an end-to-end offline reinforcement learning framework to optimize sequential notification decisions. We address the challenge of offline learning using a Double Deep Q-network method based on Conservative Q-learning that mitigates the distributional shift problem and Q-value overestimation. We illustrate our fully-deployed system and demonstrate the performance and benefits of the proposed approach through both offline and online experiments.

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