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Email Volume Optimization at LinkedIn

Published: 13 August 2016 Publication History

Abstract

Online social networking services distribute various types of messages to their members. Common types of messages include news, connection requests, membership notifications, promotions and event notifications. Such communication, if used judiciously, can provide an enormous value to members thereby keeping them engaged. However sending a message for every instance of news, connection request, or the like can result in an overwhelming number of messages in a member's mailbox. This may result in reduced effectiveness of communication if the messages are not sufficiently relevant to the member's interests. It may also result in a poor brand perception of the networking service. In this paper we discuss our strategy and experience with regard to the problem of email volume optimization at LinkedIn. In particular, we present a cost-benefit analysis of sending emails, the key factors to administer an effective volume optimization, our algorithm for volume optimization, the architecture of the supporting system and experimental results from online A/B tests.

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MP4 File (kdd2016_gupta_volume_optimization_01-acm.mp4)

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  • (2023)Generalized Causal Tree for Uplift Modeling2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386842(788-798)Online publication date: 15-Dec-2023
  • (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
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cover image ACM Conferences
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2016
2176 pages
ISBN:9781450342322
DOI:10.1145/2939672
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: 13 August 2016

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Author Tags

  1. email
  2. machine learning
  3. optimization

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KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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  • (2024)TIM: Temporal Interaction Model in Notification SystemProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3657614(1120-1124)Online publication date: 30-May-2024
  • (2023)Generalized Causal Tree for Uplift Modeling2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386842(788-798)Online publication date: 15-Dec-2023
  • (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
  • (2022)Zillow: Volume Governing for Email and Push MessagesProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547399(519-521)Online publication date: 12-Sep-2022
  • (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
  • (2021)Algorithmic Balancing of Familiarity, Similarity, & Discovery in Music RecommendationsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481893(3996-4005)Online publication date: 26-Oct-2021
  • (2021)A Novel Approach to Control Emails Notification using NLPProcedia Computer Science10.1016/j.procs.2021.05.097189(224-231)Online publication date: 2021
  • (2020)ECLIPSEProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525004(704-714)Online publication date: 13-Jul-2020
  • (2020)Bandit based Optimization of Multiple Objectives on a Music Streaming PlatformProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403374(3224-3233)Online publication date: 23-Aug-2020
  • (2020)Solving Billion-Scale Knapsack ProblemsProceedings of The Web Conference 202010.1145/3366423.3380084(3105-3111)Online publication date: 20-Apr-2020
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