research-article

Near Real-time Optimization of Activity-based Notifications

Authors:

Ajith Muralidharan,

Shaunak ChatterjeeAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 283 - 292

https://doi.org/10.1145/3219819.3219880

Published: 19 July 2018 Publication History

Abstract

In recent years, social media applications (e.g., Facebook, LinkedIn) have created mobile applications (apps) to give their members instant and real-time access from anywhere. To keep members informed and drive timely engagement, these mobile apps send event notifications. However, sending notifications for every possible event would result in too many notifications which would in turn annoy members and create a poor member experience.

In this paper, we present our strategy of optimizing notifications to balance various utilities (e.g., engagement, send volume) by formulating the problem using constrained optimization. To guarantee freshness of notifications, we implement the solution in a stream computing system in which we make multi-channel send decisions in near real-time. Through online A/B test results, we show the effectiveness of our proposed approach on tens of millions of members.

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Cited By

Fikry MInoue S(2023)Optimizing Forecasted Activity Notifications with Reinforcement LearningSensors10.3390/s2314651023:14(6510)Online publication date: 19-Jul-2023
https://doi.org/10.3390/s23146510
Zhang YZhao MLi CTou WChen HNiu DYin CHe YGuo F(2023)Online Volume Optimization for Notifications via Long Short-Term Value ModelingAdvances in Knowledge Discovery and Data Mining10.1007/978-3-031-33380-4_2(16-28)Online publication date: 27-May-2023
https://doi.org/10.1007/978-3-031-33380-4_2
Prabhakar PYuan YYang GSun WMuralidharan AZhang ARangwala H(2022)Multi-objective Optimization of Notifications Using Offline Reinforcement LearningProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539193(3752-3760)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539193
Show More Cited By

Index Terms

Near Real-time Optimization of Activity-based Notifications
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression

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cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 19 July 2018

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KDD '18

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Fikry MInoue S(2023)Optimizing Forecasted Activity Notifications with Reinforcement LearningSensors10.3390/s2314651023:14(6510)Online publication date: 19-Jul-2023
https://doi.org/10.3390/s23146510
Zhang YZhao MLi CTou WChen HNiu DYin CHe YGuo F(2023)Online Volume Optimization for Notifications via Long Short-Term Value ModelingAdvances in Knowledge Discovery and Data Mining10.1007/978-3-031-33380-4_2(16-28)Online publication date: 27-May-2023
https://doi.org/10.1007/978-3-031-33380-4_2
Prabhakar PYuan YYang GSun WMuralidharan AZhang ARangwala H(2022)Multi-objective Optimization of Notifications Using Offline Reinforcement LearningProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539193(3752-3760)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539193
Yuan YMuralidharan ANandy PCheng MPrabhakar PAl Hasan MXiong L(2022)Offline Reinforcement Learning for Mobile NotificationsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557083(3614-3623)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557083
Tu YBasu KDiCiccio CBansal RNandy PJaikumar PChatterjee S(2021)Personalized Treatment Selection using Causal HeterogeneityProceedings of the Web Conference 202110.1145/3442381.3450075(1574-1585)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3450075
Basu KGhoting AMazumder RPan YDaumé HSingh A(2020)ECLIPSEProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525004(704-714)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3525004
Yan JXu ZTiwana BChatterjee SGupta RLiu YShah MRajan STang JPrakash B(2020)Ads Allocation in Feed via Constrained OptimizationProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403391(3386-3394)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403391
Patil BMamtora HMandalya KParekh NBide P(2020)Smart Notifications Based on Total Relevancy ScoreAdvanced Computing Technologies and Applications10.1007/978-981-15-3242-9_37(391-398)Online publication date: 7-May-2020
https://doi.org/10.1007/978-981-15-3242-9_37
Tu YLo CYuan YChatterjee STeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Feedback ShapingProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330764(2241-2250)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330764

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