research-article

The Million Domain Challenge: Broadcast Email Prioritization by Cross-domain Recommendation

Authors:

Deng CaiAuthors Info & Claims

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1895 - 1904

https://doi.org/10.1145/2939672.2939863

Published: 13 August 2016 Publication History

Abstract

With email overload becoming a billion-level drag on the economy, personalized email prioritization is of urgent need to help predict the importance level of an email. Despite lots of previous effort on the topic, broadcast email, an important type of emails with its unique challenges and intriguing opportunities, has been overlooked. The most salient opportunity lies in that effective collaborative filtering can be exploited due to thousands of receivers of a typical broadcast email. However, every broadcast email is completely cold and it is very costly to obtain users' preference feedback. Fortunately, there exist up to million-level broadcast mailing lists in a real life email system. Similar mailing lists can provide useful extra information for broadcast email prioritization in a target mailing list. How to mine such useful extra information is a challenging problem that has never been touched. In this work, we propose the first broadcast email prioritization framework considering large numbers of mailing lists by formulating this problem as a cross domain recommendation problem. An optimization framework is proposed to select the optimal set of source domains considering multiple criteria including overlap of users, feedback pattern similarity and coverage of users. Our method is thoroughly evaluated on a real world industrial dataset from Samsung Electronics and is proved highly effective and outperforms all the baselines.

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Cited By

Zhao WXu CGuan ZLiu Y(2021)Multiview Concept Learning Via Deep Matrix FactorizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.297953232:2(814-825)Online publication date: Feb-2021
https://doi.org/10.1109/TNNLS.2020.2979532
Li HLiu YMamoulis NRosenblum D(2020)Translation-Based Sequential Recommendation for Complex Users on Sparse DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.290618032:8(1639-1651)Online publication date: 1-Aug-2020
https://doi.org/10.1109/TKDE.2019.2906180
Zhu YLin JHe SWang BGuan ZLiu HCai D(2020)Addressing the Item Cold-Start Problem by Attribute-Driven Active LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289153032:4(631-644)Online publication date: 1-Apr-2020
https://doi.org/10.1109/TKDE.2019.2891530
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Index Terms

The Million Domain Challenge: Broadcast Email Prioritization by Cross-domain Recommendation
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
      2. Social recommendation

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Published In

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

General Chairs:
Balaji Krishnapuram
IBM
,
Mohak Shah
Bosch
,
Program Chairs:
Alex Smola
Amazon
,
Charu Aggarwal
IBM
,
Dou Shen
Baidu
,
Rajeev Rastogi
Amazon

Copyright © 2016 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: 13 August 2016

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Funding Sources

NSERC Discovery Grant
National Science Foundation of China
National Key Technology R&D Program
National Basic Research Program of China(973 Program)

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KDD '16

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KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2016

California, San Francisco, USA

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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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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Zhao WXu CGuan ZLiu Y(2021)Multiview Concept Learning Via Deep Matrix FactorizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.297953232:2(814-825)Online publication date: Feb-2021
https://doi.org/10.1109/TNNLS.2020.2979532
Li HLiu YMamoulis NRosenblum D(2020)Translation-Based Sequential Recommendation for Complex Users on Sparse DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.290618032:8(1639-1651)Online publication date: 1-Aug-2020
https://doi.org/10.1109/TKDE.2019.2906180
Zhu YLin JHe SWang BGuan ZLiu HCai D(2020)Addressing the Item Cold-Start Problem by Attribute-Driven Active LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289153032:4(631-644)Online publication date: 1-Apr-2020
https://doi.org/10.1109/TKDE.2019.2891530
Zhu YGong YLiu QMa YOu WZhu JWang BGuan ZCai DZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRUProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357805(2585-2593)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3357805
Lian DXie XChen E(2019)Discrete Matrix Factorization and Extension for Fast Item RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2951386(1-1)Online publication date: 2019
https://doi.org/10.1109/TKDE.2019.2951386
Wang CDeng ZLai JYu P(2019)Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative FilteringIEEE Transactions on Cybernetics10.1109/TCYB.2018.284192449:7(2678-2692)Online publication date: Jul-2019
https://doi.org/10.1109/TCYB.2018.2841924
Feng SZhang HCao JYao Y(2019)Merging user social network into the random walk model for better group recommendationApplied Intelligence10.1007/s10489-018-1375-z49:6(2046-2058)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10489-018-1375-z
Xu CGuan ZZhao WNiu YWang QWang Z(2018)Deep multi-view concept learningProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304889.3305063(2898-2904)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304889.3305063
Zhu YZhu JHou JLi YWang BGuan ZCai D(2018)A brand-level ranking system with the customized attention-GRU modelProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304318(3947-3953)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304222.3304318
Wang BGuo XEster MGuan ZSingh BZhu YBu JCai DCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Device-Aware Rule Recommendation for the Internet of ThingsProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3272009(2037-2045)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3272009
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