More Web Proxy on the site http://driver.im/

research-article

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.

References

[1]

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Durán, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In Advances in Neural Information Processing Systems.

[2]

George Casella and Roger L Berger. 2002. Statistical inference .Duxbury Pacific Grove, CA.

[3]

Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In Proc. ACM Conf. Knowledge Discovery and Data Mining.

Digital Library

[4]

Beno^i t Colson, Patrice Marcotte, and Gilles Savard. 2007. An overview of bilevel optimization. Annals OR (2007).

[5]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In Proc. ACM Conf. Recommender Systems.

Digital Library

[6]

Clark R. Givens and Rae Michael Shortt. 1984. A class of Wasserstein metrics for probability distributions. The Michigan Mathematical Journal (1984).

[7]

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In Proc. Int. Joint Conf. Artificial Intelligence.

[8]

Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017a. Translation-based Recommendation. In Proc. ACM Conf. Recommender Systems.

[9]

Ruining He and Julian McAuley. 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In Proc. Int. Conf. World Wide Web.

Digital Library

[10]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017b. Neural Collaborative Filtering. In Proc. Int. Conf. World Wide Web.

Digital Library

[11]

Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast Matrix Factorization for Online Recommendation with Implicit Feedback. In Proc. ACM Int. Conf. Research and Development in Information Retrieval.

Digital Library

[12]

Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge J. Belongie, and Deborah Estrin. 2017. Collaborative Metric Learning. In Proc. Int. Conf. World Wide Web.

Digital Library

[13]

Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In Proc. IEEE Int. Conf. Data Mining.

Digital Library

[14]

Santosh Kabbur, Xia Ning, and George Karypis. 2013. FISM: factored item similarity models for top-N recommender systems. In Proc. ACM Conf. Knowledge Discovery and Data Mining.

Digital Library

[15]

Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In Proc. Int. Conf. Learning Representations.

[16]

Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. IEEE Computer (2009).

Digital Library

[17]

Mingming Li, Shuai Zhang, Fuqing Zhu, Wanhui Qian, Liangjun Zang, Jizhong Han, and Songlin Hu. 2020. Symmetric Metric Learning with Adaptive Margin for Recommendation. (2020).

[18]

Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2019. DARTS: Differentiable Architecture Search. In Proc. Int. Conf. Learning Representations.

[19]

Chen Ma, Peng Kang, and Xue Liu. 2019 a. Hierarchical Gating Networks for Sequential Recommendation. In Proc. ACM Conf. Knowledge Discovery and Data Mining.

Digital Library

[20]

Chen Ma, Peng Kang, Bin Wu, Qinglong Wang, and Xue Liu. 2019 b. Gated Attentive-Autoencoder for Content-Aware Recommendation. In Proc. ACM Int. Conf. Web Search and Data Mining.

Digital Library

[21]

Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, and Mark Coates. 2020. Memory Augmented Graph Neural Networks for Sequential Recommendation. In AAAI.

[22]

Chen Ma, Yingxue Zhang, Qinglong Wang, and Xue Liu. 2018. Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence. In Proc. ACM Int. Conf. Information and Knowledge Management.

Digital Library

[23]

Anton Mallasto and Aasa Feragen. 2017. Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes. In Advances in Neural Information Processing Systems.

[24]

Xia Ning, Christian Desrosiers, and George Karypis. 2015. A Comprehensive Survey of Neighborhood-Based Recommendation Methods. In Recommender Systems Handbook.

[25]

Rong Pan, Yunhong Zhou, Bin Cao, Nathan Nan Liu, Rajan M. Lukose, Martin Scholz, and Qiang Yang. 2008. One-Class Collaborative Filtering. In Proc. IEEE Int. Conf. Data Mining.

Digital Library

[26]

Chanyoung Park, Donghyun Kim, Xing Xie, and Hwanjo Yu. 2018. Collaborative Translational Metric Learning. In Proc. IEEE Int. Conf. Data Mining.

[27]

Steffen Rendle. 2012. Learning recommender systems with adaptive regularization. In Proc. ACM Int. Conf. Web Search and Data Mining.

Digital Library

[28]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proc. Conf. Uncertainty in Artificial Intelligence.

[29]

Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization. In Advances in Neural Information Processing Systems.

[30]

Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey E. Hinton. 2007. Restricted Boltzmann machines for collaborative filtering. In Proc. Int. Conf. Machine Learning.

[31]

Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proc. Int. Conf. World Wide Web.

Digital Library

[32]

Anshumali Shrivastava and Ping Li. 2014. Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS). In Advances in Neural Information Processing Systems.

[33]

Ankur Sinha, Pekka Malo, and Kalyanmoy Deb. 2018. A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications. IEEE Trans. Evolutionary Computation (2018).

[34]

Sashi Mohan Srivastava. 2008. A course on Borel sets .Springer Science & Business Media.

[35]

Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. 2015. End-To-End Memory Networks. In Advances in Neural Information Processing Systems.

[36]

Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, and Xiuqiang He. 2019. Multi-graph Convolution Collaborative Filtering. In Proc. IEEE Int. Conf. Data Mining.

[37]

Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018. Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking. In Proc. Int. Conf. World Wide Web.

Digital Library

[38]

Mengting Wan and Julian McAuley. 2018. Item recommendation on monotonic behavior chains. In Proc. ACM Conf. Recommender Systems.

Digital Library

[39]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative Deep Learning for Recommender Systems. In Proc. ACM Conf. Knowledge Discovery and Data Mining.

Digital Library

[40]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In Proc. ACM Int. Conf. Research and Development in Information Retrieval.

Digital Library

[41]

Jason Weston, Samy Bengio, and Nicolas Usunier. 2010. Large scale image annotation: learning to rank with joint word-image embeddings. Machine Learning (2010).

[42]

Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems. In Proc. ACM Int. Conf. Web Search and Data Mining.

Digital Library

[43]

Yujia Xie, Xiangfeng Wang, Ruijia Wang, and Hongyuan Zha. 2019. A Fast Proximal Point Method for Computing Exact Wasserstein Distance. In Proc. Conf. Uncertainty in Artificial Intelligence.

[44]

Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep Matrix Factorization Models for Recommender Systems. In Proc. Int. Joint Conf. Artificial Intelligence.

Cited By

Li HOuyang YLiu ZRong WXiong Z(2025)Wasserstein Dependent Graph Attention Network for Collaborative Filtering With UncertaintyIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.342207412:1(152-162)Online publication date: Feb-2025
https://doi.org/10.1109/TCSS.2024.3422074
Lin ZHuang WZhang HXu JLiu WLiao XWang FWang STan YLarson K(2024)Enhancing dual-target cross-domain recommendation with federated privacy-preserving learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/238(2153-2161)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/238
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
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

Recommendations

User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items
Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence

Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Noncollaborative user modeling techniques that rely on the item features can be used to recommend new items. ...
Serendipitous Personalized Ranking for Top-N Recommendation
WI-IAT '12: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01

Serendipitous recommendation has benefitted both e-retailers and users. It tends to suggest items which are both unexpected and useful to users. These items are not only profitable to the retailers but also surprisingly suitable to consumers' tastes. ...
Adaptive personalized recommendation based on adaptive learning

Collaborative filtering has been widely applied in many fields in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation-based, SVD-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

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.

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '20

Sponsor:

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

July 6 - 10, 2020

CA, Virtual Event, USA

Acceptance Rates

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

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

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

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

29
Total Citations
View Citations
1,529
Total Downloads

Downloads (Last 12 months)63
Downloads (Last 6 weeks)6

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li HOuyang YLiu ZRong WXiong Z(2025)Wasserstein Dependent Graph Attention Network for Collaborative Filtering With UncertaintyIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.342207412:1(152-162)Online publication date: Feb-2025
https://doi.org/10.1109/TCSS.2024.3422074
Lin ZHuang WZhang HXu JLiu WLiao XWang FWang STan YLarson K(2024)Enhancing dual-target cross-domain recommendation with federated privacy-preserving learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/238(2153-2161)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/238
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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten