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Locally Connected Deep Learning Framework for Industrial-scale Recommender Systems

Authors:

Minghui QiuAuthors Info & Claims

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

Pages 769 - 770

https://doi.org/10.1145/3041021.3054227

Published: 03 April 2017 Publication History

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Abstract

In this work, we propose a locally connected deep learning framework for recommender systems, which reduces the complexity of deep neural network (DNN) by two to three orders of magnitude. We further extend the framework using the idea of the recently proposed Wide&Deep model. Experiments on industrial-scale datasets show that our methods could achieve good results with much shorter runtime.

References

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H.-T. Cheng, L. Koc, J. Harmsen, et al. Wide & deep learning for recommender systems. In 1st Workshop on Deep Learning for RecSys, pages 7--10, 2016.

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K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. CoRR, 2015.

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Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521(7553):436--444, 2015.

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E. P. Xing, Q. Ho, W. Dai, et al. Petuum: A new platform for distributed machine learning on big data. IEEE Transactions on Big Data, 1(2):49--67, 2015.

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Bian ZYan XZhang JYiu MTang B(2024)QSRP: Efficient Reverse $k-\text{Ranks}$ Query Processing on High-Dimensional Embeddings2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00351(4614-4627)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00351
Aftab SRamampiaro HLangseth HRuocco M(2023)Deep Contextual Grid Triplet Network for Context-Aware RecommendationIEEE Access10.1109/ACCESS.2023.331047011(97522-97537)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3310470
Elbattah MCarette RCilia FGuérin JDequen G(2023)Applications of machine learning methods to assist the diagnosis of autism spectrum disorderNeural Engineering Techniques for Autism Spectrum Disorder, Volume 210.1016/B978-0-12-824421-0.00013-8(99-119)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-824421-0.00013-8
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Index Terms

Locally Connected Deep Learning Framework for Industrial-scale Recommender Systems
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

April 2017

1738 pages

ISBN:9781450349147

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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WWW '17

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IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Companion Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Bian ZYan XZhang JYiu MTang B(2024)QSRP: Efficient Reverse $k-\text{Ranks}$ Query Processing on High-Dimensional Embeddings2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00351(4614-4627)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00351
Aftab SRamampiaro HLangseth HRuocco M(2023)Deep Contextual Grid Triplet Network for Context-Aware RecommendationIEEE Access10.1109/ACCESS.2023.331047011(97522-97537)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3310470
Elbattah MCarette RCilia FGuérin JDequen G(2023)Applications of machine learning methods to assist the diagnosis of autism spectrum disorderNeural Engineering Techniques for Autism Spectrum Disorder, Volume 210.1016/B978-0-12-824421-0.00013-8(99-119)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-824421-0.00013-8
Abinaya SAlphonse AAbirami SKavithadevi M(2023)Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive AutoencoderNeural Processing Letters10.1007/s11063-023-11163-x55:5(6843-6864)Online publication date: 13-Feb-2023
https://doi.org/10.1007/s11063-023-11163-x
Zhou JZhang XZhu ZLan XFu LWang HWen H(2022)Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity PredictionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2021.306395232:5(2535-2549)Online publication date: May-2022
https://doi.org/10.1109/TCSVT.2021.3063952
Han HWang CZhao YShu MWang WMin Y(2022)SSLE: A framework for evaluating the “Filter Bubble” effect on the news aggregator and recommendersWorld Wide Web10.1007/s11280-022-01031-425:3(1169-1195)Online publication date: 8-Mar-2022
https://doi.org/10.1007/s11280-022-01031-4
Zhang KQian HCui QLiu QLi LZhou JMa JChen ELewin-Eytan LCarmel DYom-Tov EAgichtein EGabrilovich E(2021)Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR PredictionProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441761(984-992)Online publication date: 8-Mar-2021
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Ahmadian Yazdi HSeyyed Mahdavi Chabok SKheirabadi M(2021)Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention TechniqueApplied Artificial Intelligence10.1080/08839514.2021.200529836:1Online publication date: 11-Dec-2021
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Ahmed ASaleem KKhalid ORashid U(2021)On deep neural network for trust aware cross domain recommendations in E-commerceExpert Systems with Applications10.1016/j.eswa.2021.114757174(114757)Online publication date: Jul-2021
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Fang X(2021)Making recommendations using transfer learningNeural Computing and Applications10.1007/s00521-021-05730-3Online publication date: 28-Jan-2021
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A Novel Two-stage Learning Pipeline for Deep Neural Networks

Symmetric Rectified Linear Units for Fully Connected Deep Models

Mapping ultrasound-based articulatory images and vowel sounds with a deep neural network framework

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