research-article

Graph-based Regularization on Embedding Layers for Recommendation

Authors:

Yan ZhangAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 39, Issue 1

Article No.: 2, Pages 1 - 27

https://doi.org/10.1145/3414067

Published: 05 September 2020 Publication History

Abstract

Neural networks have been extensively used in recommender systems. Embedding layers are not only necessary but also crucial for neural models in recommendation as a typical discrete task. In this article, we argue that the widely used l₂ regularization for normal neural layers (e.g., fully connected layers) is not ideal for embedding layers from the perspective of regularization theory in Reproducing Kernel Hilbert Space. More specifically, the l₂ regularization corresponds to the inner product and the distance in the Euclidean space where correlations between discrete objects (e.g., items) are not well captured. Inspired by this observation, we propose a graph-based regularization approach to serve as a counterpart of the l₂ regularization for embedding layers. The proposed regularization incurs almost no extra computational overhead especially when being trained with mini-batches. We also discuss its relationships to other approaches (namely, data augmentation, graph convolution, and joint learning) theoretically. We conducted extensive experiments on five publicly available datasets from various domains with two state-of-the-art recommendation models. Results show that given a kNN (k-nearest neighbor) graph constructed directly from training data without external information, the proposed approach significantly outperforms the l₂ regularization on all the datasets and achieves more notable improvements for long-tail users and items.

References

[1]

Mauricio A. Alvarez, Lorenzo Rosasco, Neil D. Lawrence, et al. 2012. Kernels for vector-valued functions: A review. Found. Trends Mach. Learn. 4, 3 (2012), 195--266.

Digital Library

[2]

Onureena Banerjee, Laurent El Ghaoui, and Alexandre d’Aspremont. 2008. Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. J. Mach. Learn. Res. 9 (Mar. 2008), 485--516.

Digital Library

[3]

Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261.

[4]

Rianne van den Berg, Thomas N. Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263.

[5]

Chris M. Bishop. 1995. Training with noise is equivalent to Tikhonov regularization. Neural Comput. 7, 1 (1995), 108--116.

Digital Library

[6]

O. Celma. 2010. Music Recommendation and Discovery in the Long Tail. Springer.

[7]

Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, and Yi-Hsuan Yang. 2019. Collaborative similarity embedding for recommender systems. In Proceedings of the World Wide Web Conference (WWW’19). ACM, New York, NY, 2637--2643.

Digital Library

[8]

Zhiyong Cheng, Jialie Shen, Lei Zhu, Mohan S. Kankanhalli, and Liqiang Nie. 2017. Exploiting music play sequence for music recommendation. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17), Vol. 17. 3654--3660.

[9]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys’16). ACM, 191--198.

Digital Library

[10]

H. E. Egilmez, E. Pavez, and A. Ortega. 2017. Graph learning from data under laplacian and structural constraints. IEEE J. Select. Top. Signal Process. 11, 6 (Sep. 2017), 825--841.

[11]

Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In Proceedings of the World Wide Web Conference (WWW’19). 417--426.

Digital Library

[12]

F. Maxwell Harper and Joseph A. Konstan. 2016. The movielens datasets: History and context. ACM Trans. Interact. Intell. Syst. 5, 4 (2016), 19.

[13]

Ruining He, Chen Fang, Zhaowen Wang, and Julian McAuley. 2016. Vista: A visually, socially, and temporally aware model for artistic recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems (Recsys’16). ACM, 309--316.

Digital Library

[14]

Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017. Translation-based recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, 161--169.

Digital Library

[15]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). International World Wide Web Conferences Steering Committee, 173--182.

Digital Library

[16]

Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, New York, NY, 843--852.

Digital Library

[17]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In Proceedings of 4th International Conference on Learning Representation (ICLR’16).

[18]

Liang Hu, Longbing Cao, Jian Cao, Zhiping Gu, Guandong Xu, and Jie Wang. 2017. Improving the quality of recommendations for users and items in the tail of distribution. ACM Trans. Info. Syst. 35, 3 (2017), 1--37.

Digital Library

[19]

Yogesh Jhamb, Travis Ebesu, and Yi Fang. 2018. Attentive contextual denoising autoencoder for recommendation. In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval (SIGIR’18). ACM, 27--34.

Digital Library

[20]

Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the IEEE International Conference on Data Mining (ICDM’18). IEEE, 197--206.

[21]

George S Kimeldorf and Grace Wahba. 1970. A correspondence between Bayesian estimation on stochastic processes and smoothing by splines. Ann. Math. Stat. 41, 2 (1970), 495--502.

[22]

Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the International Conference on Learning Representations (ICLR’17).

[23]

Lingpeng Kong, Gabor Melis, Wang Ling, Lei Yu, and Dani Yogatama. 2019. Variational smoothing in recurrent neural network language models. In Proceedings of the International Conference on Learning Representations (ICLR’19).

[24]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 8 (2009), 30--37.

Digital Library

[25]

Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In Proceedings of the ACM on Conference on Information and Knowledge Management (CIKM’17). ACM, New York, NY, 1419--1428.

Digital Library

[26]

Dawen Liang, Jaan Altosaar, Laurent Charlin, and David M. Blei. 2016. Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In Proceedings of the 10th ACM Conference on Recommender Systems (Recsys’16). ACM, 59--66.

[27]

Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the World Wide Web Conference on World Wide Web (WWW’18). International World Wide Web Conferences Steering Committee, 689--698.

Digital Library

[28]

Daryl Lim, Julian McAuley, and Gert Lanckriet. 2015. Top-n recommendation with missing implicit feedback. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys’15). ACM, 309--312.

Digital Library

[29]

Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 1 (2003), 76--80.

Digital Library

[30]

Hao Ma, Irwin King, and Michael R. Lyu. 2007. Effective missing data prediction for collaborative filtering. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’07). 39--46.

[31]

Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King. 2011. Recommender systems with social regularization. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM’11). ACM, 287--296.

Digital Library

[32]

Benjamin M. Marlin. 2004. Modeling user rating profiles for collaborative filtering. In Advances in Neural Information Processing Systems (NIPS’04). MIT Press, 627--634.

[33]

Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’15). ACM, New York, NY, 43--52.

[34]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NIPS’13). MIT Press, 3111--3119.

[35]

Rong Pan, Yunhong Zhou, Bin Cao, Nathan N. Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08). IEEE, 502--511.

Digital Library

[36]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’14). ACM, 701--710.

Digital Library

[37]

Nikhil Rao, Hsiang-Fu Yu, Pradeep K. Ravikumar, and Inderjit S. Dhillon. 2015. Collaborative filtering with graph information: Consistency and scalable methods. In Advances in Neural Information Processing Systems (NIPS’15). MIT Press, 2107--2115.

Digital Library

[38]

Chuan Shi, Binbin Hu, Xin Zhao, and Philip Yu. 2018. Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31, 2 (2018), 357–370.

Digital Library

[39]

Chuan Shi, Zhiqiang Zhang, Ping Luo, Philip S. Yu, Yading Yue, and Bin Wu. 2015. Semantic path-based personalized recommendation on weighted heterogeneous information networks. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM’15). ACM, 453--462.

Digital Library

[40]

Alexander J. Smola and Risi Kondor. 2003. Kernels and regularization on graphs. In Learning Theory and Kernel Machines. Springer, 144--158.

[41]

Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM’19). ACM, New York, NY.

Digital Library

[42]

Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S. Yu, and Tianyi Wu. 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4, 11 (2011), 992--1003.

Digital Library

[43]

Jiaxi Tang and Ke Wang. 2018. Personalized top-N sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM’18). ACM, New York, NY, 565--573.

Digital Library

[44]

Thanh Tran, Kyumin Lee, Yiming Liao, and Dongwon Lee. 2018. Regularizing matrix factorization with user and item embeddings for recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM’18). ACM, 687--696.

Digital Library

[45]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems (NIPS’17). MIT Press, 5998--6008.

[46]

Saurabh Verma and Zhi-Li Zhang. 2019. Stability and generalization of graph convolutional neural networks. In Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD’19).

Digital Library

[47]

Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018. Ripple network: Propagating user preferences on the knowledge graph for recommender systems. Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM’18).

Digital Library

[48]

Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2019. Exploring high-order user preference on the knowledge graph for recommender systems. ACM Trans. Info. Syst. 37, 3 (2019), 1--26.

Digital Library

[49]

Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the World Wide Web Conference on World Wide Web (WWW’18). International World Wide Web Conferences Steering Committee, 1835--1844.

Digital Library

[50]

Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2019. Multi-task feature learning for knowledge graph enhanced recommendation. In Proceedings of the World Wide Web Conference (WWW’19). ACM, New York, NY, 2000--2010.

Digital Library

[51]

Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019. Knowledge graph convolutional networks for recommender systems. In Proceedings of the World Wide Web Conference (WWW’19). ACM, 3307--3313.

Digital Library

[52]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19).

Digital Library

[53]

Zihan Wang, Ziheng Jiang, Zhaochun Ren, Jiliang Tang, and Dawei Yin. 2018. A path-constrained framework for discriminating substitutable and complementary products in e-commerce. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM’18). ACM, 619--627.

Digital Library

[54]

Jason Wei and Kai Zou. 2019. EDA: Easy data augmentation techniques for boosting performance on text classification tasks. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). Association for Computational Linguistics, Hong Kong, China, 6383--6389.

[55]

Christopher K. I. Williams and Carl Edward Rasmussen. 2006. Gaussian Processes for Machine Learning. Vol. 2. MIT Press, Cambridge, MA.

[56]

Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In Proceedings of the 36th International Conference on Machine Learning (ICML’19). PMLR, 6861--6871.

[57]

Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 346--353.

Digital Library

[58]

Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM’16). ACM, 153--162.

Digital Library

[59]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2019. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596.

[60]

Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, and Andrew Y. Ng. 2017. Data noising as smoothing in neural network language models. In Proceedings of the International Conference on Learning Representations (ICLR’17).

[61]

Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose. 2019. Relational collaborative filtering: Modeling multiple item relations for recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19).

Digital Library

[62]

Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, and Richang Hong. 2019. Deep item-based collaborative filtering for top-n recommendation. ACM Trans. Info. Syst. 37, 3 (2019), 1--25.

Digital Library

[63]

Jheng-Hong Yang, Chih-Ming Chen, Chuan-Ju Wang, and Ming-Feng Tsai. 2018. HOP-rec: High-order proximity for implicit recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (Recsys’18). ACM, 140--144.

Digital Library

[64]

Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18). ACM, 974--983.

Digital Library

[65]

Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. A dynamic recurrent model for next basket recommendation. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’16). ACM, New York, NY, 729--732.

Digital Library

[66]

Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, and Jiawei Han. 2014. Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM’14). ACM, 283--292.

Digital Library

[67]

Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, and Xiangnan He. 2019. A simple convolutional generative network for next item recommendation. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM’19). ACM, New York, NY, 582--590.

Digital Library

[68]

Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). ACM, 353--362.

Digital Library

[69]

Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Advances in Neural Information Processing Systems (NIPS’15). MIT Press, 649--657.

[70]

Yongfeng Zhang, Qingyao Ai, Xu Chen, and W. Bruce Croft. 2017. Joint representation learning for top-n recommendation with heterogeneous information sources. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM’17). ACM, 1449--1458.

[71]

Yuan Zhang, Xiaoran Xu, Hanning Zhou, and Yan Zhang. 2020. Distilling structured knowledge into embeddings for explainable and accurate recommendation. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM’2020).

Digital Library

[72]

Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, and Dik Lun Lee. 2017. Meta-graph-based recommendation fusion over heterogeneous information networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’17). ACM, 635--644.

Digital Library

[73]

Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, and Philip S. Yu. 2018. Spectral collaborative filtering. In Proceedings of the 12th ACM Conference on Recommender Systems (Recsys’18). ACM, New York, NY, 311--319.

[74]

Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. 2018. Learning tree-based deep model for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18). ACM, 1079--1088.

Digital Library

Cited By

Zhang SJiang TKuang KFeng FYu JMa JZhao ZZhu JYang HChua TWu F(2023)SLED: Structure Learning based Denoising for RecommendationACM Transactions on Information Systems10.1145/361138542:2(1-31)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1145/3611385
Zhang QZhu HWang PChen EXiong H(2023)Hierarchical Wi-Fi Trajectory Embedding for Indoor User Mobility Pattern AnalysisProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962377:2(1-21)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596237
Han JLiu HLiu SChen XTan NChai HXiong HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)iETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival EstimationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599842(4100-4111)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599842
Show More Cited By

Index Terms

Graph-based Regularization on Embedding Layers for Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Regularization
    2. Machine learning approaches
      1. Learning latent representations
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Knowledge embedding towards the recommendation with sparse user-item interactions
ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Recently, many researchers in recommender systems have realized that encoding user-item interactions based on deep neural networks (DNNs) promotes collaborative-filtering (CF)'s performance. Nonetheless, those DNN-based models' performance is still ...
Embedding with autoencoder regularization
ECMLPKDD'13: Proceedings of the 2013th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part III

The problem of embedding arises in many machine learning applications with the assumption that there may exist a small number of variabilities which can guarantee the "semantics" of the original high-dimensional data. Most of the existing embedding ...
Structured sparsity with group-graph regularization
AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence

In many learning tasks with structural properties, structural sparsity methods help induce sparse models, usually leading to better interpretability and higher generalization performance. One popular approach is to use group sparsity regularization that ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 39, Issue 1

January 2021

329 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3423044

Editor:
Min Zhang
Tsinghua University, China

Issue’s Table of Contents

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2020

Accepted: 01 July 2020

Revised: 01 June 2020

Received: 01 January 2020

Published in TOIS Volume 39, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Key Research and Development Program of China
MOE-ChinaMobile Program
NSFC

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
821
Total Downloads

Downloads (Last 12 months)57
Downloads (Last 6 weeks)3

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang SJiang TKuang KFeng FYu JMa JZhao ZZhu JYang HChua TWu F(2023)SLED: Structure Learning based Denoising for RecommendationACM Transactions on Information Systems10.1145/361138542:2(1-31)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1145/3611385
Zhang QZhu HWang PChen EXiong H(2023)Hierarchical Wi-Fi Trajectory Embedding for Indoor User Mobility Pattern AnalysisProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962377:2(1-21)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596237
Han JLiu HLiu SChen XTan NChai HXiong HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)iETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival EstimationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599842(4100-4111)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599842
Wu DHe YLuo X(2023)A Graph-Incorporated Latent Factor Analysis Model for High-Dimensional and Sparse DataIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.329286611:4(907-917)Online publication date: Oct-2023
https://doi.org/10.1109/TETC.2023.3292866
Li WSitu YDing LChen YYang Q(2023)MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal–Organic FrameworksACS Applied Materials & Interfaces10.1021/acsami.3c1179015:51(59887-59894)Online publication date: 12-Dec-2023
https://doi.org/10.1021/acsami.3c11790
Zhu TChen JWang ZWu D(2022)Regularized Framework on Heterogeneous Hypergraph Model for Personal RecommendationTheoretical Computer Science10.1007/978-981-19-8152-4_11(160-174)Online publication date: 10-Dec-2022
https://doi.org/10.1007/978-981-19-8152-4_11
da Silva ASpinosa E(2022)A Sequential Recommender System with Embeddings Based on GraphSage AggregatorsIntelligent Systems10.1007/978-3-031-21689-3_1(1-15)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-21689-3_1
He MHan TDing T(2022)Multilevel Feature Interaction Learning for Session-Based Recommendation via Graph Neural NetworksWeb Engineering10.1007/978-3-031-09917-5_3(31-46)Online publication date: 5-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-09917-5_3
Duran PKaratzoglou AVitria JXin XArapakis I(2021)Graph Convolutional Embeddings for Recommender SystemsIEEE Access10.1109/ACCESS.2021.30966099(100173-100184)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3096609
Wu SSun FZhang WXie XCui B(undefined)Graph Neural Networks in Recommender Systems: A SurveyACM Computing Surveys10.1145/3535101
https://dl.acm.org/doi/10.1145/3535101

View Options

Get Access

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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents