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TBPR: Trinity Preference based Bayesian Personalized Ranking for Multivariate Implicit Feedback

Authors:

Zhu Sun,

Yun LiuAuthors Info & Claims

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

Pages 305 - 306

https://doi.org/10.1145/2930238.2930272

Published: 13 July 2016 Publication History

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Abstract

In e-commerce systems, user preference can be inferred from multivariate implicit feedback (i.e., actions). However, most methods merely focus on homogeneous implicit feedback (i.e., purchase). In this paper, we adopt another two typical actions, i.e., view and like, as auxiliaries to enhance purchase recommendation, whereby a trinity Bayesian personalized ranking (TBPR) method is proposed. Specifically, we introduce trinity preference to investigate the difference of users' preference among three types of items: 1) items with purchase action; 2) items with only auxiliary actions; 3) items without any action. Empirical study on the real-world dataset demonstrates that our method significantly outperforms state-of-the-art algorithms.

References

[1]

W. Pan and L. Chen. Gbpr: Group preference based bayesian personalized ranking for one-class collaborative filtering. In IJCAI, 2013.

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[2]

W. Pan, H. Zhong, C. Xu, and Z. Ming. Adaptive bayesian personalized ranking for heterogeneous implicit feedbacks. KBS, 73:173--180, 2015.

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[3]

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, 2009.

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Cited By

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Guo GZhou HChen BLiu ZXu XChen XDong ZHe X(2022)IPGAN: Generating Informative Item Pairs by Adversarial SamplingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.302857233:2(694-706)Online publication date: Feb-2022
https://doi.org/10.1109/TNNLS.2020.3028572
Zhang QRen F(2021)Double bayesian pairwise learning for one-class collaborative filteringKnowledge-Based Systems10.1016/j.knosys.2021.107339(107339)Online publication date: Jul-2021
https://doi.org/10.1016/j.knosys.2021.107339
Yu WQin ZHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Sampler Design for Implicit Feedback Data by Noisy-label Robust LearningProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401155(861-870)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401155
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Index Terms

TBPR: Trinity Preference based Bayesian Personalized Ranking for Multivariate Implicit Feedback
1. Information systems

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Published In

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

July 2016

366 pages

ISBN:9781450343688

DOI:10.1145/2930238

General Chairs:
Julita Vassileva
University of Saskatchewan, Canada
,
James Blustein
Dalhousie University, Canada
,
Program Chairs:
Lora Aroyo
VU Amsterdam, the Netherlands
,
Sidney D'Mello
University of Notre Dame, USA

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

Publication History

Published: 13 July 2016

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UMAP '16

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UMAP '16: User Modeling, Adaptation and Personalization Conference

July 13 - 17, 2016

Nova Scotia, Halifax, Canada

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UMAP '16 Paper Acceptance Rate 21 of 123 submissions, 17%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

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Guo GZhou HChen BLiu ZXu XChen XDong ZHe X(2022)IPGAN: Generating Informative Item Pairs by Adversarial SamplingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.302857233:2(694-706)Online publication date: Feb-2022
https://doi.org/10.1109/TNNLS.2020.3028572
Zhang QRen F(2021)Double bayesian pairwise learning for one-class collaborative filteringKnowledge-Based Systems10.1016/j.knosys.2021.107339(107339)Online publication date: Jul-2021
https://doi.org/10.1016/j.knosys.2021.107339
Yu WQin ZHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Sampler Design for Implicit Feedback Data by Noisy-label Robust LearningProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401155(861-870)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401155
Zhou XSun ZGuo GLiu Y(2020)Modelling Temporal Dynamics and Repeated Behaviors for RecommendationAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47426-3_15(181-193)Online publication date: 6-May-2020
https://doi.org/10.1007/978-3-030-47426-3_15
Jiang YLv MSun JLiu Y(2018)A Bayesian Personalized Ranking Algorithm Based on Tag Preference2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)10.1109/DSC.2018.00075(465-471)Online publication date: Jun-2018
https://doi.org/10.1109/DSC.2018.00075
Jia RLi R(2018)Modeling User Purchase Preference Based on Implicit Feedback*2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD))10.1109/CSCWD.2018.8465380(832-836)Online publication date: May-2018
https://doi.org/10.1109/CSCWD.2018.8465380
Gurbanov TRicci FShin SShin DLencastre M(2017)Action prediction models for recommender systems based on collaborative filtering and sequence mining hybridizationProceedings of the Symposium on Applied Computing10.1145/3019612.3019759(1655-1661)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3019612.3019759

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Index Terms

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Implicit Feedback Awareness for Session Based Recommendation in E-Commerce

Graph-based recommendation by trust

Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems