Abstract
App push notifications are an essential tool for app developers to engage with their users actively and provide them with timely and relevant information about the app. However, determining the proper volume of notifications sent to each user is a key challenge for improving user experience, particularly for new users whose preferences on push notifications are unknown. In this paper, we address the problem of app notification volume optimization for newly onboarded users and propose a systematic approach to solve this problem. We incorporate a multi-task learning technique to accurately modeling both the short-term and long-term effects of different volumes of push notifications, and utilize online linear programming to achieve real-time notification allocation with volume constraints. We have conducted both offline and online experiments to evaluate the effectiveness of our method, and the results demonstrate that our approach dramatically improves multiple core metrics of the user experience, such as daily active users.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agrawal, S., Wang, Z., Ye, Y.: A dynamic near-optimal algorithm for online linear programming. Oper. Res. 62(4), 876–890 (2014)
Gao, Y., et al.: Near real-time optimization of activity-based notifications. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 283–292 (2018)
Gupta, R., Liang, G., Tseng, H.P., Holur Vijay, R.K., Chen, X., Rosales, R.: Email volume optimization at Linkedin. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97–106 (2016)
Ho, B.J., Balaji, B., Koseoglu, M., Srivastava, M.: Nurture: notifying users at the right time using reinforcement learning. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, pp. 1194–1201 (2018)
Johnson, M.A., Moradi, M.H.: PID Control. Springer, London (2005). https://doi.org/10.1007/1-84628-148-2
Jouini, W., Ernst, D., Moy, C., Palicot, J.: Upper confidence bound based decision making strategies and dynamic spectrum access. In: 2010 IEEE International Conference on Communications, pp. 1–5. IEEE (2010)
Liu, Y., et al.: Personalized execution time optimization for the scheduled jobs. arXiv preprint arXiv:2203.06158 (2022)
Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., Chi, E.H.: Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1930–1939 (2018)
Ma, X., et al.: Entire space multi-task model: an effective approach for estimating post-click conversion rate. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1137–1140 (2018)
O’Brien, C., Wu, H., Zhai, S., Guo, D., Shi, W., Hunt, J.J.: Should i send this notification? Optimizing push notifications decision making by modeling the future. arXiv preprint arXiv:2202.08812 (2022)
Okoshi, T., Tsubouchi, K., Tokuda, H.: Real-world product deployment of adaptive push notification scheduling on smartphones. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2792–2800 (2019)
Sutton, R., Fraser, K., Conlan, O.: A reinforcement learning and synthetic data approach to mobile notification management. In: Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia, pp. 155–164 (2019)
Tang, H., Liu, J., Zhao, M., Gong, X.: Progressive layered extraction (PLE): a novel multi-task learning (MTL) model for personalized recommendations. In: Fourteenth ACM Conference on Recommender Systems, pp. 269–278 (2020)
Visuri, A., van Berkel, N., Okoshi, T., Goncalves, J., Kostakos, V.: Understanding smartphone notifications’ user interactions and content importance. Int. J. Hum. Comput. Stud. 128, 72–85 (2019)
Yuan, Y., Muralidharan, A., Nandy, P., Cheng, M., Prabhakar, P.: Offline reinforcement learning for mobile notifications. arXiv preprint arXiv:2202.03867 (2022)
Zhao, B., Narita, K., Orten, B., Egan, J.: Notification volume control and optimization system at Pinterest. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1012–1020 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y. et al. (2023). Online Volume Optimization for Notifications via Long Short-Term Value Modeling. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13937. Springer, Cham. https://doi.org/10.1007/978-3-031-33380-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-33380-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33379-8
Online ISBN: 978-3-031-33380-4
eBook Packages: Computer ScienceComputer Science (R0)