research-article

MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection

Authors:

Pengxiang Cheng,

Zhenguo LiAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 2507 - 2513

https://doi.org/10.1145/3366423.3379999

Published: 20 April 2020 Publication History

Abstract

Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user. We observe this ubiquitous phenomenon on both public and private datasets and address the model selection problem in pursuit of optimizing the quality of recommendation for each user. We propose a meta-learning framework to facilitate user-level adaptive model selection in recommender systems. In this framework, a collection of recommenders is trained with data from all users, on top of which a model selector is trained via meta-learning to select the best single model for each user with the user-specific historical data. We conduct extensive experiments on two public datasets and a real-world production dataset, demonstrating that our proposed framework achieves improvements over single model baselines and sample-level model selector in terms of AUC and LogLoss. In particular, the improvements may lead to huge profit gain when deployed in online recommender systems.

References

[1]

Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, and Nando de Freitas. 2016. Learning to learn by gradient descent by gradient descent. In NIPS.

[2]

Robin Burke. 2002. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-adapted Interaction 12, 4 (2002), 331–370.

Digital Library

[3]

Olivier Chapelle, Eren Manavoglu, and Romer Rosales. 2015. Simple and scalable response prediction for display advertising. ACM Transactions on Intelligent Systems and Technology (TIST) 5, 4(2015), 61.

Digital Library

[4]

Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He. 2018. Federated Meta-Learning with Fast Convergence and Efficient Communication. arXiv preprint arXiv:1802.07876(2018).

[5]

Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang. 2019. A closer look at few-shot classification. arXiv preprint arXiv:1904.04232(2019).

[6]

Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. ACM, 7–10.

Digital Library

[7]

Andrew Collins, Dominika Tkaczyk, and Joeran Beel. 2018. One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level. arXiv preprint arXiv:1805.12118(2018).

[8]

Tiago Cunha, Carlos Soares, and Acplf De Carvalho. 2018. Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem for Collaborative Filtering. Information Sciences 423(2018), 128–144.

Digital Library

[9]

Tiago Cunha, Carlos Soares, and André CPLF de Carvalho. 2016. Selecting collaborative filtering algorithms using metalearning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 393–409.

[10]

Simon Dooms. 2013. Dynamic generation of personalized hybrid recommender systems. In Proceedings of the 7th ACM conference on Recommender systems. ACM, 443–446.

Digital Library

[11]

Michael Ekstrand and John Riedl. 2012. When recommenders fail: predicting recommender failure for algorithm selection and combination. In Proceedings of the sixth ACM conference on Recommender systems. ACM, 233–236.

Digital Library

[12]

Michael D Ekstrand, F Maxwell Harper, Martijn C Willemsen, and Joseph A Konstan. 2014. User perception of differences in recommender algorithms. In Proceedings of the 8th ACM Conference on Recommender systems. ACM, 161–168.

Digital Library

[13]

Michael D Ekstrand, Daniel Kluver, F Maxwell Harper, and Joseph A Konstan. 2015. Letting users choose recommender algorithms: An experimental study. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 11–18.

Digital Library

[14]

Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 1126–1135.

Digital Library

[15]

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247(2017).

[16]

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context.

[17]

Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web. 507–517.

Digital Library

[18]

Ruining He and Julian McAuley. 2016. VBPR: visual bayesian personalized ranking from implicit feedback. In Thirtieth AAAI Conference on Artificial Intelligence.

Digital Library

[19]

Xiangnan He and Tat-Seng Chua. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 355–364.

Digital Library

[20]

Yimin Huang, Weiran Huang, Liang Li, and Zhenguo Li. 2019. Meta-Learning PAC-Bayes Priors in Model Averaging. arXiv preprint arXiv:1912.11252(2019).

[21]

Yuchin Juan, Damien Lefortier, and Olivier Chapelle. 2017. Field-aware factorization machines in a real-world online advertising system. In Proceedings of the 26th International Conference on World Wide Web Companion. 680–688.

Digital Library

[22]

Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. 2016. Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 43–50.

Digital Library

[23]

Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).

[24]

Mu Li, Tong Zhang, Yuqiang Chen, and Alexander J Smola. 2014. Efficient mini-batch training for stochastic optimization. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 661–670.

Digital Library

[25]

Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. 2017. Meta-sgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835(2017).

[26]

H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, 2013. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1222–1230.

Digital Library

[27]

H Brendan McMahan and Matthew Streeter. 2010. Adaptive bound optimization for online convex optimization. arXiv preprint arXiv:1002.4908(2010).

[28]

Tsendsuren Munkhdalai and Hong Yu. 2017. Meta networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2554–2563.

Digital Library

[29]

Alex Nichol and John Schulman. 2018. Reptile: a scalable metalearning algorithm. arXiv preprint arXiv:1803.02999(2018).

[30]

Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016. Product-based neural networks for user response prediction. In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 1149–1154.

[31]

Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. In ICLR.

[32]

Steffen Rendle. 2010. Factorization machines. In 2010 IEEE International Conference on Data Mining. IEEE, 995–1000.

Digital Library

[33]

Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM, 175–186.

Digital Library

[34]

Jürgen Schmidhuber. 1987. Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook. Ph.D. Dissertation. Technische Universität München.

[35]

Ying Shan, T Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and JC Mao. 2016. Deep crossing: Web-scale modeling without manually crafted combinatorial features. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 255–262.

Digital Library

[36]

Joseph Sill, Gábor Takács, Lester Mackey, and David Lin. 2009. Feature-weighted linear stacking. arXiv preprint arXiv:0911.0460(2009).

[37]

Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. In Advances in Neural Information Processing Systems. 4077–4087.

[38]

Sebastian Thrun and Lorien Pratt. 2012. Learning to learn. Springer Science & Business Media.

[39]

Oriol Vinyals, Charles Blundell, Tim Lillicrap, and Daan Wierstra. 2016. Matching networks for one shot learning. In NIPS.

[40]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 1235–1244.

Digital Library

[41]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. arXiv preprint arXiv:1905.08108(2019).

[42]

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1059–1068.

Digital Library

Cited By

Wang RPrabhakar PSrivastava GWang TJalali ZBharill VOuyang YNigam AVenugopalan DGupta ABorisyuk FKeerthi SMuralidharan ABaeza-Yates RBonchi F(2024)LiMAML: Personalization of Deep Recommender Models via Meta LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671599(5882-5892)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671599
Niu YXing XJia ZLiu RXin MCui JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Diffusion Recommendation with Implicit Sequence InfluenceCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651951(1719-1725)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651951
Ye HZhou DHong LLi ZWei XZhan D(2024)Contextualizing Meta-Learning via Learning to DecomposeIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.331742546:1(117-133)Online publication date: Jan-2024
https://doi.org/10.1109/TPAMI.2023.3317425
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Index Terms

MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Cited By

Wang RPrabhakar PSrivastava GWang TJalali ZBharill VOuyang YNigam AVenugopalan DGupta ABorisyuk FKeerthi SMuralidharan ABaeza-Yates RBonchi F(2024)LiMAML: Personalization of Deep Recommender Models via Meta LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671599(5882-5892)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671599
Niu YXing XJia ZLiu RXin MCui JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Diffusion Recommendation with Implicit Sequence InfluenceCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651951(1719-1725)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651951
Ye HZhou DHong LLi ZWei XZhan D(2024)Contextualizing Meta-Learning via Learning to DecomposeIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.331742546:1(117-133)Online publication date: Jan-2024
https://doi.org/10.1109/TPAMI.2023.3317425
Dai SShao NZhu JZhang XDong ZXu JDai QWen J(2024)Modeling User Attention in Music Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00064(761-774)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00064
Jiang JZhang PLuo YLi CKim JZhang KWang SXie XKim SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614773(976-986)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614773
Palumbo GCarneiro DGuimares MAlves VNovais P(2023)Algorithm Recommendation and Performance Prediction Using Meta-LearningInternational Journal of Neural Systems10.1142/S012906572350011933:03Online publication date: 1-Feb-2023
https://doi.org/10.1142/S0129065723500119
Du YZhu XChen LFang ZGao Y(2023)MetaKG: Meta-Learning on Knowledge Graph for Cold-Start RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.316877535:10(9850-9863)Online publication date: 1-Oct-2023
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Chen HYin RYang Z(2023)Edge-Cloud Collaborative High-Quality Recommendation: A Meta-Learning Approach2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)10.1109/ISKE60036.2023.10481220(554-561)Online publication date: 17-Nov-2023
https://doi.org/10.1109/ISKE60036.2023.10481220
Bharati SMondal MPodder PPrasath V(2022)Federated learning: Applications, challenges and future directionsInternational Journal of Hybrid Intelligent Systems10.3233/HIS-22000618:1-2(19-35)Online publication date: 31-May-2022
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Xie RWang YWang RLu YZou YXia FLin LSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Long Short-Term Temporal Meta-learning in Online RecommendationProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498371(1168-1176)Online publication date: 11-Feb-2022
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