research-article

Neural Tensor Factorization for Temporal Interaction Learning

Authors:

Nitesh V. ChawlaAuthors Info & Claims

WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

Pages 537 - 545

https://doi.org/10.1145/3289600.3290998

Published: 30 January 2019 Publication History

Abstract

Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling relational data (user-item interactions). However, they are also limited in their assumption of static or sequential modeling of relational data as they do not account for evolving users' preference over time as well as changes in the underlying factors that drive the change in user-item relationship over time. We address these limitations by proposing a Neural network based Tensor Factorization (NTF) model for predictive tasks on dynamic relational data. The NTF model generalizes conventional tensor factorization from two perspectives: First, it leverages the long short-term memory architecture to characterize the multi-dimensional temporal interactions on relational data. Second, it incorporates the multi-layer perceptron structure for learning the non-linearities between different latent factors. Our extensive experiments demonstrate the significant improvement in both the rating prediction and link prediction tasks on various dynamic relational data by our NTF model over both neural network based factorization models and other traditional methods.

References

[1]

Lada A Adamic and Eytan Adar. 2003. Friends and neighbors on the web. Social networks, Vol. 25, 3 (2003), 211--230.

[2]

Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 8 (2013), 1798--1828.

Digital Library

[3]

Preeti Bhargava, Thomas Phan, Jiayu Zhou, and Juhan Lee. 2015. Who, what, when, and where: Multi-dimensional collaborative recommendations using tensor factorization on sparse user-generated data. In WWW. ACM, 130--140.

Digital Library

[4]

Jiajun Bu, Xin Shen, Bin Xu, Chun Chen, Xiaofei He, and Deng Cai. 2016. Improving collaborative recommendation via user-item subgroups. TKDE, Vol. 28, 9 (2016), 2363--2375.

Digital Library

[5]

Iván Cantador, Alejandro Bellog'in, and David Vallet. 2010. Content-based recommendation in social tagging systems. In Recsys. ACM, 237--240.

Digital Library

[6]

J Douglas Carroll and Jih-Jie Chang. 1970. Analysis of individual differences in multidimensional scaling via an N-way generalization of "Eckart-Young" decomposition. Psychometrika, Vol. 35, 3 (1970), 283--319.

[7]

Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In SIGIR. ACM, 335--344.

Digital Library

[8]

Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. JMLR, Vol. 12, Aug (2011), 2493--2537.

Digital Library

[9]

Gintare Karolina Dziugaite and Daniel M Roy. 2015. Neural network matrix factorization. ICLR (2015).

[10]

F Maxwell Harper and Joseph A Konstan. 2016. The movielens datasets: History and context. TIIS, Vol. 5, 4 (2016), 19.

Digital Library

[11]

Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW. ACM, 507--517.

Digital Library

[12]

Xiangnan He and Tat-Seng Chua. 2017. Neural Factorization Machines for Sparse Predictive Analytics. In SIGIR. ACM.

Digital Library

[13]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. ACM, 173--182.

Digital Library

[14]

Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017. Collaborative metric learning. In WWW. ACM, 193--201.

Digital Library

[15]

Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML. 448--456.

Digital Library

[16]

Rafal Jozefowicz, Wojciech Zaremba, and Ilya Sutskever. 2015. An empirical exploration of recurrent network architectures. In ICML . 2342--2350.

Digital Library

[17]

Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. 2016. Convolutional matrix factorization for document context-aware recommendation. In Recsys. ACM, 233--240.

Digital Library

[18]

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

[19]

Tamara G Kolda and Brett W Bader. 2009. Tensor decompositions and applications. SIAM review, Vol. 51, 3 (2009), 455--500.

Digital Library

[20]

Yehuda Koren. 2008. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. In KDD '08. ACM, New York, NY, USA, 426--434.

Digital Library

[21]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer, Vol. 42, 8 (Aug. 2009), 30--37.

Digital Library

[22]

Xiaopeng Li and James She. 2017. Collaborative Variational Autoencoder for Recommender Systems. In KDD. ACM, 305--314.

Digital Library

[23]

David Liben-Nowell and Jon Kleinberg. 2007. The link-prediction problem for social networks. journal of the Association for Information Science and Technology, Vol. 58, 7 (2007), 1019--1031.

Digital Library

[24]

Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. SoRec: Social Recommendation Using Probabilistic Matrix Factorization. In CIKM . ACM, New York, NY, USA, 931--940.

Digital Library

[25]

Andriy Mnih and Ruslan R Salakhutdinov. 2008. Probabilistic matrix factorization. In NIPS. 1257--1264.

Digital Library

[26]

Roberto Rigamonti, Matthew A Brown, et almbox. 2011. Are sparse representations really relevant for image classification?. In CVPR. IEEE, 1545--1552.

Digital Library

[27]

Tara N Sainath, Brian Kingsbury, Vikas Sindhwani, Ebru Arisoy, and Bhuvana Ramabhadran. 2013. Low-rank matrix factorization for deep neural network training with high-dimensional output targets. In ICASSP. IEEE, 6655--6659.

[28]

Ruslan Salakhutdinov and Andriy Mnih. 2008. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In ICML. ACM, 880--887.

Digital Library

[29]

Salvatore Scellato, Anastasios Noulas, et almbox. 2011. Exploiting place features in link prediction on location-based social networks. In KDD. ACM, 1046--1054.

Digital Library

[30]

Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. Autorec: Autoencoders meet collaborative filtering. In WWW. ACM, 111--112.

Digital Library

[31]

Ting-Yi Shih, Ting-Chang Hou, Jian-De Jiang, Yen-Chieh Lien, Chia-Rui Lin, and Pu-Jen Cheng. 2016. Dynamically Integrating Item Exposure with Rating Prediction in Collaborative Filtering. In SIGIR . ACM, 813--816.

Digital Library

[32]

Hidetoshi Shimodaira. 2000. Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of statistical planning and inference, Vol. 90, 2 (2000), 227--244.

[33]

Yang Song, Ali Mamdouh Elkahky, and Xiaodong He. 2016. Multi-rate deep learning for temporal recommendation. In SIGIR. ACM, 909--912.

Digital Library

[34]

Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In NIPS. 2643--2651.

Digital Library

[35]

Hao Wang, Xingjian Shi, and Dit-Yan Yeung. 2017. Relational Deep Learning: A Deep Latent Variable Model for Link Prediction. In AAAI . 2688--2694.

Digital Library

[36]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In KDD. ACM, 1235--1244.

Digital Library

[37]

Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing. 2017a. Recurrent recommender networks. In WSDM. ACM, 495--503.

Digital Library

[38]

Xian Wu, Yuxiao Dong, Chao Huang, Jian Xu, Dong Wang, and Nitesh V Chawla. 2017b. UAPD: Predicting Urban Anomalies from Spatial-Temporal Data. In ECML/PKDD. Springer, 622--638.

[39]

Yao Wu, Christopher DuBois, Alice X Zheng, et almbox. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In WSDM. ACM, 153--162.

Digital Library

[40]

Liang Xiong, Xi Chen, et almbox. 2010. Temporal collaborative filtering with bayesian probabilistic tensor factorization. In SDM . SIAM, 211--222.

[41]

Xuchao Zhang, Liang Zhao, Arnold P Boedihardjo, Chang-Tien Lu, and Naren Ramakrishnan. 2017. Spatiotemporal Event Forecasting from Incomplete Hyper-local Price Data. In CIKM. ACM, 507--516.

Digital Library

Cited By

Ou ZFu XNorbäck DLin RWen JSun Y(2025)MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic AnalysisMetabolites10.3390/metabo1501005115:1(51)Online publication date: 14-Jan-2025
https://doi.org/10.3390/metabo15010051
Zhang SChu HLi JZhou YWang SSun QNejdl WAuer SCha MMoens MNajork M(2025)DeMBR: Denoising Model with Memory Pruning and Semantic Guidance for Multi-Behavior RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703532(521-529)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703532
Lin QLi XXie KWen JHe SXie GFan XFeng Q(2025)Network Monitoring Data Recovery Based on Flexible Bi-Directional ModelIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.350707812:2(623-635)Online publication date: Mar-2025
https://doi.org/10.1109/TNSE.2024.3507078
Show More Cited By

Index Terms

Neural Tensor Factorization for Temporal Interaction Learning
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Temporal data

Recommendations

Adversarial tensor factorization for context-aware recommendation
RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Contextual factors such as time, location, or tag, can affect user preferences for a particular item. Context-aware recommendations are thus critical to improve both quality and explainability of recommender systems, compared to traditional ...
Pairwise interaction tensor factorization for personalized tag recommendation
WSDM '10: Proceedings of the third ACM international conference on Web search and data mining

Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to ...
Learning Higher-Order Interactions for User and Item Profiling Based on Tensor Factorization
IUI '15: Proceedings of the 20th International Conference on Intelligent User Interfaces

User profiling techniques play a central role in many Recommender Systems (RS). In recent years, multidimensional data are getting increasing attention for making recommendations. Additional metadata help algorithms better understanding users' behaviors ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

January 2019

874 pages

ISBN:9781450359405

DOI:10.1145/3289600

General Chairs:
J. Shane Culpepper
RMIT University
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Paul N. Bennett
Microsoft
,
Kristina Lerman
University of Southern California

Copyright © 2019 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: 30 January 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WSDM '19

Sponsor:

WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining

February 11 - 15, 2019

Melbourne VIC, Australia

Acceptance Rates

WSDM '19 Paper Acceptance Rate 84 of 511 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

68
Total Citations
View Citations
943
Total Downloads

Downloads (Last 12 months)63
Downloads (Last 6 weeks)14

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ou ZFu XNorbäck DLin RWen JSun Y(2025)MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic AnalysisMetabolites10.3390/metabo1501005115:1(51)Online publication date: 14-Jan-2025
https://doi.org/10.3390/metabo15010051
Zhang SChu HLi JZhou YWang SSun QNejdl WAuer SCha MMoens MNajork M(2025)DeMBR: Denoising Model with Memory Pruning and Semantic Guidance for Multi-Behavior RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703532(521-529)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703532
Lin QLi XXie KWen JHe SXie GFan XFeng Q(2025)Network Monitoring Data Recovery Based on Flexible Bi-Directional ModelIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.350707812:2(623-635)Online publication date: Mar-2025
https://doi.org/10.1109/TNSE.2024.3507078
Dai YShen JZhai ZLiu DChen JSun YLi PZhang JZhang KSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)High-order contrastive learning with fine-grained comparative levels for sparse ordinal tensor completionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692461(9856-9871)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692461
Selamat AIbrahim RAlrashidi MFujita H(2024)Social Recommender System Based on CNN Incorporating Tagging and Contextual FeaturesJournal of Cases on Information Technology10.4018/JCIT.33552426:1(1-20)Online publication date: 7-Jan-2024
https://dl.acm.org/doi/10.4018/JCIT.335524
Miyata T(2024)Traffic matrix completion by weighted tensor nuclear norm minimization and time slicingNonlinear Theory and Its Applications, IEICE10.1587/nolta.15.31115:2(311-323)Online publication date: 2024
https://doi.org/10.1587/nolta.15.311
Qiu RJang JLin XLiu LTong H(2024)TUCKET: A Tensor Time Series Data Structure for Efficient and Accurate Factor Analysis over Time RangesProceedings of the VLDB Endowment10.14778/3704965.370498017:13(4746-4759)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.14778/3704965.3704980
Ahn DSaini UPapalexakis EPayani ASerra ESpezzano F(2024)Neural Additive Tensor Decomposition for Sparse TensorsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679833(14-23)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679833
Jang JPark YKang USerra ESpezzano F(2024)Fast and Accurate PARAFAC2 Decomposition for Time Range Queries on Irregular TensorsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679735(962-972)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679735
Wang HXie KWen JZhang GLiang WXie GLi K(2024)PetTC: Pairwise Joint Embedding Based Contrastive Tensor Completion for Network Traffic Monitoring ServicesIEEE Transactions on Services Computing10.1109/TSC.2024.3517331(1-14)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3517331
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