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Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

Authors:

Mark CoatesAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 1036 - 1044

https://doi.org/10.1145/3394486.3403147

Published: 20 August 2020 Publication History

Abstract

Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them. Although matrix factorization and deep learning based methods have proved effective in user preference modeling, they violate the triangle inequality and fail to capture fine-grained preference information. To tackle this, we develop a distance-based recommendation model with several novel aspects: (i) each user and item are parameterized by Gaussian distributions to capture the learning uncertainties; (ii) an adaptive margin generation scheme is proposed to generate the margins regarding different training triplets; (iii) explicit user-user/item-item similarity modeling is incorporated in the objective function. The Wasserstein distance is employed to determine preferences because it obeys the triangle inequality and can measure the distance between probabilistic distributions. Via a comparison using five real-world datasets with state-of-the-art methods, the proposed model outperforms the best existing models by 4-22% in terms of recall@K on Top-K recommendation.

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Cited By

Liu WChen CLiao XHu MSu JTan YWang FChua TNgo CKa-Wei Lee RKumar RLauw H(2024)User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645331(334-343)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645331
Zheng XLiu WChen CSu JLiao XHu MTan Y(2024)Mining User Consistent and Robust Preference for Unified Cross Domain RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.344658136:12(8758-8772)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3446581
Chen YFang YZhang YMa CHong YKing I(2024)Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive HashingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342589136:12(9418-9432)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3425891
Show More Cited By

Index Terms

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 ACM.

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Published: 20 August 2020

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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

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Cited By

Liu WChen CLiao XHu MSu JTan YWang FChua TNgo CKa-Wei Lee RKumar RLauw H(2024)User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645331(334-343)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645331
Zheng XLiu WChen CSu JLiao XHu MTan Y(2024)Mining User Consistent and Robust Preference for Unified Cross Domain RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.344658136:12(8758-8772)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3446581
Chen YFang YZhang YMa CHong YKing I(2024)Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive HashingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342589136:12(9418-9432)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3425891
Zhang XXu BMa FLi CYang LLin H(2024)Beyond Co-Occurrence: Multi-Modal Session-Based RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330999536:4(1450-1462)Online publication date: Apr-2024
https://doi.org/10.1109/TKDE.2023.3309995
Wang CWang MWang XTan Y(2024)IPSRM: An intent perceived sequential recommendation modelJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10220636:9(102206)Online publication date: Nov-2024
https://doi.org/10.1016/j.jksuci.2024.102206
Wang JChen GXin KFei Z(2024)Metric learning with adversarial hard negative samples for tag recommendationThe Journal of Supercomputing10.1007/s11227-024-06274-880:14(21475-21507)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1007/s11227-024-06274-8
Yuan HZhao PXian XLiu GLiu YSheng VZhao LElkind E(2023)Sequential recommendation with probabilistic logical reasoningProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/270(2432-2440)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/270
Luo XWu DGu YChen CLiu LMa JZhang MDeng MHuang JHua X(2023)Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit RecommendationACM Transactions on Knowledge Discovery from Data10.1145/361131018:1(1-26)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3611310
Jeunen OGoethals B(2023)Pessimistic Decision-Making for Recommender SystemsACM Transactions on Recommender Systems10.1145/35680291:1(1-27)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3568029
Fan ZLiu ZPeng HYu P(2023)Mutual Wasserstein Discrepancy Minimization for Sequential RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583529(1375-1385)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583529
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