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.

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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 SKarras OCha 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
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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

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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.

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Published: 30 January 2019

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

February 11 - 15, 2019

Melbourne VIC, Australia

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

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

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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 SKarras OCha 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
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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
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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
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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
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