research-article

Identifying Users behind Shared Accounts in Online Streaming Services

Authors:

Wei WangAuthors Info & Claims

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Pages 65 - 74

https://doi.org/10.1145/3209978.3210054

Published: 27 June 2018 Publication History

Abstract

Online streaming services are prevalent. Major service providers, such as Netflix (for movies) and Spotify (for music), usually have a large customer base. More often than not, users may share an account. This has attracted increasing attention recently, as account sharing not only compromises the service provider's financial interests but also impairs the performance of recommendation systems and consequently the quality of service provided to the users. To address this issue, this paper focuses on the problem of user identification in shared accounts. Our goal is three-fold: (1) Given an account, along with its historical session logs, we identify a set of users who share such account; (2) Given a new session issued by an account, we find the corresponding user among the identified users of such account; (3) We aim to boost the performance of item recommendation by user identification. While the mapping between users and accounts is unknown, we propose an unsupervised learning-based framework, Session-based Heterogeneous graph Embedding for User Identification (SHE-UI), to differentiate and model the preferences of users in an account, and to group sessions by these users. In SHE-UI, a heterogeneous graph is constructed to represent items such as songs and their available metadata such as artists, genres, and albums. An item-based session embedding technique is proposed using a normalized random walk in the heterogeneous graph. Our experiments conducted on two large-scale music streaming datasets, Last.fm and KKBOX, show that SHE-UI not only accurately identifies users, but also significantly improves the performance of item recommendation over the state-of-the-art methods.

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https://dl.acm.org/doi/10.1145/3589335.3648346
Guo LZhang JTang LChen TZhu LYin H(2024)Time Interval-Enhanced Graph Neural Network for Shared-Account Cross-Domain Sequential RecommendationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.320153335:3(4002-4016)Online publication date: Mar-2024
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Index Terms

Identifying Users behind Shared Accounts in Online Streaming Services
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

June 2018

1509 pages

ISBN:9781450356572

DOI:10.1145/3209978

General Chairs:
Kevyn Collins-Thompson
University of Michigan, United States
,
Qiaozhu Mei
University of Michigan, United States
,
Program Chairs:
Brian Davison
Lehigh University, United States
,
Yiqun Liu
Tsinghua University, China
,
Emine Yilmaz
University College London, United Kingdom

Copyright © 2018 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

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Published: 27 June 2018

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SIGIR

SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval

July 8 - 12, 2018

MI, Ann Arbor, USA

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SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Song KShang WZhang YYi TWang X(2024)Security issues of news data dissemination in internet environmentJournal of Cloud Computing10.1186/s13677-024-00632-w13:1Online publication date: 22-Mar-2024
https://doi.org/10.1186/s13677-024-00632-w
Chen HLi CChen TChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Skewness-aware Boosting Regression Trees for Customer Contribution Prediction in Financial Precision MarketingCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3648346(461-470)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648346
Guo LZhang JTang LChen TZhu LYin H(2024)Time Interval-Enhanced Graph Neural Network for Shared-Account Cross-Domain Sequential RecommendationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.320153335:3(4002-4016)Online publication date: Mar-2024
https://doi.org/10.1109/TNNLS.2022.3201533
Chen HLi CChen TFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Semi-supervised Curriculum Ensemble Learning for Financial Precision MarketingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615251(3773-3777)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615251
Singh KTsai YLi CCha MLin SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)GraphFC: Customs Fraud Detection with Label ScarcityProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614690(4829-4835)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614690
Sun WMa MRen PLin YChen ZRen ZMa Jde Rijke M(2023)Parallel Split-Join Networks for Shared Account Cross-Domain Sequential RecommendationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313092735:4(4106-4123)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2021.3130927
Gao CWang MChen J(2023)Item Recommendation on Shared Accounts Through User IdentificationSocial Media Processing10.1007/978-981-99-7596-9_5(63-76)Online publication date: 15-Nov-2023
https://doi.org/10.1007/978-981-99-7596-9_5
Wu CHsieh HJiang JYang YShei CChen Y(2022)MUFFLE: Multi-Modal Fake News Influence Estimator on TwitterApplied Sciences10.3390/app1201045312:1(453)Online publication date: 4-Jan-2022
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Sailaja NFowler A(2022)An Exploration of Account Sharing Practices on Media PlatformsProceedings of the 2022 ACM International Conference on Interactive Media Experiences10.1145/3505284.3529974(141-150)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3505284.3529974
Li CHsu CZhang Y(2022)FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph EmbeddingsACM Transactions on Intelligent Systems and Technology10.1145/349516313:1(1-21)Online publication date: 28-Feb-2022
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