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Recurrent Recommender Networks

Authors:

Alexander J. Smola,

How JingAuthors Info & Claims

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

Pages 495 - 503

https://doi.org/10.1145/3018661.3018689

Published: 02 February 2017 Publication History

Abstract

Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function.

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Cited By

Alrashidi MSelamat AIbrahim RFujita 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://doi.org/10.4018/JCIT.335524
Song WXing X(2024)Dual-Tower Counterfactual Session-Aware Recommender SystemEntropy10.3390/e2606051626:6(516)Online publication date: 14-Jun-2024
https://doi.org/10.3390/e26060516
Yuan QZhu MLi YLiu HGuo S(2024)Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate PredictionApplied Sciences10.3390/app1407276014:7(2760)Online publication date: 26-Mar-2024
https://doi.org/10.3390/app14072760
Show More Cited By

Index Terms

Recurrent Recommender Networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Abstract
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Information

Published In

cover image ACM Conferences

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

February 2017

868 pages

ISBN:9781450346757

DOI:10.1145/3018661

General Chairs:
Maarten de Rijke
University of Amsterdam
,
Milad Shokouhi
Microsoft
,
Program Chairs:
Andrew Tomkins
Google
,
Min Zhang
Tsinghua University

Copyright © 2017 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 02 February 2017

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Google
Bloomberg
Adobe
National Science Foundation

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WSDM 2017

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WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining

February 6 - 10, 2017

Cambridge, United Kingdom

Acceptance Rates

WSDM '17 Paper Acceptance Rate 80 of 505 submissions, 16%;

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

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Cited By

Alrashidi MSelamat AIbrahim RFujita 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://doi.org/10.4018/JCIT.335524
Song WXing X(2024)Dual-Tower Counterfactual Session-Aware Recommender SystemEntropy10.3390/e2606051626:6(516)Online publication date: 14-Jun-2024
https://doi.org/10.3390/e26060516
Yuan QZhu MLi YLiu HGuo S(2024)Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate PredictionApplied Sciences10.3390/app1407276014:7(2760)Online publication date: 26-Mar-2024
https://doi.org/10.3390/app14072760
Zhang YWang ZYu WHu LJiang PGai KChen X(2024)Soft Contrastive Sequential RecommendationACM Transactions on Information Systems10.1145/366532542:6(1-28)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3665325
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Sahebi SYao MZhao SFeyzi Behnagh R(2024)MoMENt: Marked Point Processes with Memory-Enhanced Neural Networks for User Activity ModelingACM Transactions on Knowledge Discovery from Data10.1145/364950418:6(1-32)Online publication date: 29-Feb-2024
https://dl.acm.org/doi/10.1145/3649504
Luo TLiu YPan S(2024)Collaborative Sequential Recommendations via Multi-view GNN-transformersACM Transactions on Information Systems10.1145/364943642:6(1-27)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3649436
Yang SMa WSun PAi QLiu YCai MZhang MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation with Latent Relations based on Large Language ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657762(335-344)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657762
Xia JLi DGu HLu TZhang PShang LGu NAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Neural Kalman Filtering for Robust Temporal RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635837(836-845)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635837
Xiao CJi WZhang YLv S(2024)PIDKG: Propagating Interaction Influence on the Dynamic Knowledge Graph for RecommendationACM Transactions on the Web10.1145/359331418:2(1-26)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3593314
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