abstract

RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS)

Authors:

Alexandros Karatzoglou,

Lior RokachAuthors Info & Claims

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Pages 415 - 416

https://doi.org/10.1145/2959100.2959202

Published: 07 September 2016 Publication History

Get Access

Abstract

We believe that Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex tasks such as computer vision, natural language processing and speech recognition. Despite this, only little work has been published on Deep Learning methods for Recommender Systems. Notable recent application areas are music recommendation, news recommendation, and session-based recommendation. The aim of the workshop is to encourage the application of Deep Learning techniques in Recommender Systems, to promote research in deep learning methods for Recommender Systems, and to bring together researchers from the Recommender Systems and Deep Learning communities.

References

[1]

A. M. Elkahky, Y. Song, and X. He. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In WWW'15: 24th Int. Conf. on World Wide Web}, pages 278--288, 201

Digital Library

Google Scholar

[2]

R. He and J. McAuley. VBPR: Visual Bayesian Personalized Ranking from implicit feedback. CoRR, 1510.01784, 2015.

Google Scholar

[3]

B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk. Session-based recommendations with recurrent neural networks. International Conference on Learning Representations, 2016.

Google Scholar

[4]

J. McAuley, C. Targett, Q. Shi, and A. van den Hengel. Image-based recommendations on styles and substitutes. In SIGIR'15: 38th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pages 43--52, 2015.

Digital Library

Google Scholar

[5]

R. Salakhutdinov, A. Mnih, and G. Hinton. Restricted boltzmann machines for collaborative filtering. In ICML'07: 24th Int. Conf. on Machine Learning, pages 791--798, 2007.

Digital Library

Google Scholar

[6]

A. Van den Oord, S. Dieleman, and B. Schrauwen. Deep content-based music recommendation. In Advances in Neural Information Processing Systems, pages 2643--2651, 2013.

Digital Library

Google Scholar

[7]

H. Wang, N. Wang, and D.-Y. Yeung. Collaborative deep learning for recommender systems. In KDD'15: 21th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 1235--1244, 2015.

Digital Library

Google Scholar

[8]

Y. Wu, C. DuBois, A. X. Zheng, and M. Ester. Collaborative denoising auto-encoders for top-N recommender systems. In WSDM'16: 9th ACM Int. Conf. on Web Search and Data Mining, pages 153--162, 2016.

Digital Library

Google Scholar

Cited By

View all

Zhirui LDakun ZXiang LXue LYihong W(2024)Research on Adaptive System of Warehouse Energy Management System Using Gradient Boosting Tree Algorithm2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)10.1109/ICPICS62053.2024.10796830(1643-1648)Online publication date: 26-Jul-2024
https://doi.org/10.1109/ICPICS62053.2024.10796830
Nahta RChauhan GMeena YGopalani D(2024)Deep learning with the generative models for recommender systems: A surveyComputer Science Review10.1016/j.cosrev.2024.10064653(100646)Online publication date: Aug-2024
https://doi.org/10.1016/j.cosrev.2024.100646
Jangid MKumar R(2024)Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic reviewMultimedia Tools and Applications10.1007/s11042-024-20262-384:5(2293-2325)Online publication date: 16-Oct-2024
https://doi.org/10.1007/s11042-024-20262-3
Show More Cited By

Index Terms

RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS)
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

Recommendations

Deep Learning for Recommender Systems
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex machine learning tasks such as computer vision, natural language ...
A review on deep learning for recommender systems: challenges and remedies

Recommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer users. Although existing recommender systems are successful in ...
A Deep Autoencoder-Based Hybrid Recommender System

Recommender systems build their suggestions on rating data, given explicitly or implicitly by users on items. These ratings create a huge sparse user-item rating matrix which opens two challenges for researchers on the field. The first challenge is ...

Comments

Information & Contributors

Information

Published In

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

September 2016

490 pages

ISBN:9781450340359

DOI:10.1145/2959100

General Chairs:
Shilad Sen
Macalester College, USA
,
Werner Geyer
IBM Research Cambridge, USA
,
Program Chairs:
Jill Freyne
CSIRO, Australia
,
Pablo Castells
Universidad Autónoma de Madrid, Spain

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.

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2016

Check for updates

Author Tags

Qualifiers

Abstract

Funding Sources

Seventh Framework Programme

Conference

RecSys '16

Sponsor:

SIGCHI

RecSys '16: Tenth ACM Conference on Recommender Systems

September 15 - 19, 2016

Massachusetts, Boston, USA

Acceptance Rates

RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
945
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhirui LDakun ZXiang LXue LYihong W(2024)Research on Adaptive System of Warehouse Energy Management System Using Gradient Boosting Tree Algorithm2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)10.1109/ICPICS62053.2024.10796830(1643-1648)Online publication date: 26-Jul-2024
https://doi.org/10.1109/ICPICS62053.2024.10796830
Nahta RChauhan GMeena YGopalani D(2024)Deep learning with the generative models for recommender systems: A surveyComputer Science Review10.1016/j.cosrev.2024.10064653(100646)Online publication date: Aug-2024
https://doi.org/10.1016/j.cosrev.2024.100646
Jangid MKumar R(2024)Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic reviewMultimedia Tools and Applications10.1007/s11042-024-20262-384:5(2293-2325)Online publication date: 16-Oct-2024
https://doi.org/10.1007/s11042-024-20262-3
Hennekes SFrasincar FHong JLanperne MPark JCerny TShahriar H(2023)Weighted Neural Collaborative Filtering: Deep Implicit Recommendation with Weighted Positive and Negative FeedbackProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577619(1799-1808)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3555776.3577619
Li JZhou Z(2022)Design of Human Resource Management System Based on Deep LearningComputational Intelligence and Neuroscience10.1155/2022/91228812022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9122881
Sun J(2022)Machine Learning-Driven Enterprise Human Resource Management Optimization and Its ApplicationComputational Intelligence and Neuroscience10.1155/2022/25414212022(1-9)Online publication date: 1-Aug-2022
https://doi.org/10.1155/2022/2541421
Wen HLin YWan HGuo SWu FWu LSong CXu Y(2022)DeepRoute+: Modeling Couriers’ Spatial-temporal Behaviors and Decision Preferences for Package Pick-up Route PredictionACM Transactions on Intelligent Systems and Technology10.1145/348100613:2(1-23)Online publication date: 5-Jan-2022
https://dl.acm.org/doi/10.1145/3481006
Chen SChoubey BSingh V(2021)A neural network based price sensitive recommender model to predict customer choices based on price effectJournal of Retailing and Consumer Services10.1016/j.jretconser.2021.10257361(102573)Online publication date: Jul-2021
https://doi.org/10.1016/j.jretconser.2021.102573
Raza SDing C(2021)News recommender system: a review of recent progress, challenges, and opportunitiesArtificial Intelligence Review10.1007/s10462-021-10043-xOnline publication date: 21-Jul-2021
https://doi.org/10.1007/s10462-021-10043-x
Lin YHuang YChen P(2019)Employment Recommendation Algorithm Based on Ensemble Learning2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT)10.1109/ICCASIT48058.2019.8973135(267-271)Online publication date: Oct-2019
https://doi.org/10.1109/ICCASIT48058.2019.8973135
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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Deep Learning for Recommender Systems

A review on deep learning for recommender systems: challenges and remedies

A Deep Autoencoder-Based Hybrid Recommender System

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations