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PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network

Authors:

Zeinab Taghavi,

Evren Korpeoglu,

Jingrui HeAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 2409 - 2419

https://doi.org/10.1145/3447548.3467234

Published: 14 August 2021 Publication History

Abstract

Recommender systems are powerful tools for information filtering with the ever-growing amount of online data. Despite its success and wide adoption in various web applications and personalized products, many existing recommender systems still suffer from multiple drawbacks such as large amount of unobserved feedback, poor model convergence, etc. These drawbacks of existing work are mainly due to the following two reasons: first, the widely used negative sampling strategy, which treats the unlabeled entries as negative samples, is invalid in real-world settings; second, all training samples are retrieved from the discrete observations, and the underlying true distribution of the users and items is not learned.

In this paper, we address these issues by developing a novel framework named PURE, which trains an unbiased positive-unlabeled discriminator to distinguish the true relevant user-item pairs against the ones that are non-relevant, and a generator that learns the underlying user-item continuous distribution. For a comprehensive comparison, we considered 14 popular baselines from 5 different categories of recommendation approaches. Extensive experiments on two public real-world data sets demonstrate that PURE achieves the best performance in terms of 8 ranking based evaluation metrics.

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Index Terms

PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 ACM.

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Published: 14 August 2021

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Agriculture and Food Research Initiative (AFRI)
National Institute of Food and Agriculture
NSF (National Science Foundation)

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KDD '21

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KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2021

Virtual Event, Singapore

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

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Cited By

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https://dl.acm.org/doi/10.1145/3589335.3648346
Liu ZLuo W(2023)FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial NetworkApplied Sciences10.3390/app1313797513:13(7975)Online publication date: 7-Jul-2023
https://doi.org/10.3390/app13137975
Ma HXie RMeng LChen XZhang XLin LZhou J(2023)Exploring False Hard Negative Sample in Cross-Domain RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608791(502-514)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608791
Zamzam OAkrami HLeahy R(2023)Learning From Positive and Unlabeled Data Using Observer-GANICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10094818(1-5)Online publication date: 4-Jun-2023
https://doi.org/10.1109/ICASSP49357.2023.10094818
Chen SQiu YLi JFang KFang K(2023)Precision marketing for financial industry using a PU-learning recommendation methodJournal of Business Research10.1016/j.jbusres.2023.113771160(113771)Online publication date: May-2023
https://doi.org/10.1016/j.jbusres.2023.113771
Papič AKononenko IBosnić Z(2023)Conditional generative positive and unlabeled learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120046224:COnline publication date: 15-Aug-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120046
Choi JPark JKim WPark JSuh YSung M(2023)PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU LabelsMachine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track10.1007/978-3-031-43427-3_15(243-258)Online publication date: 18-Sep-2023
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Gerych WHartvigsen TBuquicchio LAgu ERundensteiner EAl Hasan MXiong L(2022)Robust Recurrent Classifier Chains for Multi-Label Learning with Missing LabelsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557438(582-591)Online publication date: 17-Oct-2022
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Zhou YWu JWang HHe JAl Hasan MXiong L(2022)Adversarial Robustness through Bias Variance DecompositionProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557232(2753-2762)Online publication date: 17-Oct-2022
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