invited-talk

Zillow: Volume Governing for Email and Push Messages

Authors:

Eric Paul Nichols,

Ruomeng Xu,

Balasubramanian Thiagarajan,

Shruti KamathAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 519 - 521

https://doi.org/10.1145/3523227.3547399

Published: 13 September 2022 Publication History

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Abstract

This talk describes the system used at Zillow to govern the quantity of email and push messages sent to users. Emphasis is given to practical issues and lessons learned in running the system in production.

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MP4 File (Zillow Volume Governing for Email and Push Messages.mp4)

Video presentation to accompany our accepted poster for RecSys 2022

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10.07 MB

References

[1]

Rupesh Gupta, Guanfeng Liang, Hsiao-Ping Tseng, Ravi Kiran Holur Vijay, Xiayu Chen, and Romer Rosales. 2016. Email Volume Optimization at LinkedIn. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD ’16). Association for Computing Machinery, New York, NY, USA, 97–106. https://doi.org/10.1145/2939672.2939692

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[2]

Sangdi Lin. 2020. Predicting Sparse Down-Funnel Events in Home Shopping with Transfer and Multi-target Learning. Retrieved June 1, 2022 from https://medium.com/zillow-tech-hub/predicting-sparse-down-funnel-events-in-home-shopping-with-transfer-and-multi-target-learning-e6cd7f7de989

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[3]

Yu Zhang and Qiang Yang. 2017. A Survey on Multi-Task Learning. CoRR abs/1707.08114(2017). arXiv:1707.08114http://arxiv.org/abs/1707.08114

Google Scholar

[4]

Bo Zhao, Koichiro Narita, Burkay Orten, and John Egan. 2018. Notification Volume Control and Optimization System at Pinterest. In Proceedings of the 4th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD ’18). Association for Computing Machinery, New York, NY, USA, 1012–1020. https://doi.org/10.1145/3219819.3219906

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Index Terms

Zillow: Volume Governing for Email and Push Messages
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization

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Information

Published In

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 13 September 2022

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Invited-talk
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RecSys '22

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Abstract

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Understanding email use: predicting action on a message

Preventing Spam Email by Delivery Limitation in RMX

Dates and times in email messages

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