abstract

DLRS 2017: Second Workshop on Deep Learning for Recommender Systems

Authors:

Balázs Hidasi,

Alexandros Karatzoglou,

Oren Sar-Shalom,

Sander Dieleman,

Bracha Shapira,

Domonkos TikkAuthors Info & Claims

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

Pages 370 - 371

https://doi.org/10.1145/3109859.3109953

Published: 27 August 2017 Publication History

Abstract

Deep learning methods became widely popular in the recommender systems community in 2016, in part thanks to the previous event of the DLRS workshop series. Now, deep learning has been embedded in the main conference as well and initial research directions have started forming, so the role of DLRS 2017 is to encourage starting new research directions, incentivize the application of very recent techniques from deep learning, and provide a venue for specialized discussion of this topic.

References

[1]

Trapit Bansal, David Belanger, and Andrew McCallum. 2016. Ask the GRU: Multi-task Learning for Deep Text Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). 107--114.

Digital Library

[2]

Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, and others. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 7--10.

Digital Library

[3]

Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. CoRR 1406.1078 (2014). http://arxiv.org/abs/1406.1078

[4]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). 191--198.

Digital Library

[5]

Hanjun Dai, Yichen Wang, Rakshit Trivedi, and Le Song. 2016. Recurrent coevolutionary latent feature processes for continuous-time recommendation. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 29--34.

Digital Library

[6]

Mihajlo Grbovic, Vladan Radosavljevic, Nemanja Djuric, Narayan Bhamidipati, Jaikit Savla, Varun Bhagwan, and Doug Sharp. 2015. E-commerce in your inbox: Product recommendations at scale. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1809--1818.

Digital Library

[7]

Ruining He and Julian McAuley. 2016. VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback. In AAAI. 144--150.

Digital Library

[8]

Balázs Hidasi and Alexandros Karatzoglou. 2017. Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. arXiv preprint arXiv:1706.03847 (2017).

[9]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939; ICLR 2016 (2015).

[10]

Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016. Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). 241--248.

Digital Library

[11]

Alexandros Karatzoglou, Balázs Hidasi, Domonkos Tikk, Oren Sar-Shalom, Haggai Roitman, Bracha Shapira, and Lior Rokach. 2016. RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS). In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). 415--416.

Digital Library

[12]

Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 43--52.

Digital Library

[13]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. CoRR 1310.4546 (2013). http://arxiv.org/abs/1310.4546

[14]

Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. arXiv preprint arXiv:1706.04148 (2017).

[15]

Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. 2007. Restricted Boltzmann machines for collaborative filtering. In ICML'07: 24th Int. Conf. on Machine Learning. 791--798.

Digital Library

[16]

Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved Recurrent Neural Networks for Session-based Recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (DLRS 2016). 17--22.

Digital Library

[17]

Bartłomiej Twardowski. 2016. Modelling Contextual Information in SessionAware Recommender Systems with Neural Networks. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). 273--276.

Digital Library

[18]

Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In Advances in Neural Information Processing Systems. 2643--2651.

Digital Library

[19]

Flavian Vasile, Elena Smirnova, and Alexis Conneau. 2016. Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). 225--232.

Digital Library

[20]

Jeroen B. P. Vuurens, Martha Larson, and Arjen P. de Vries. 2016. Exploring Deep Space: Learning Personalized Ranking in a Semantic Space. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (DLRS 2016). 23--28.

Digital Library

[21]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1235--1244.

Digital Library

[22]

Hao Wang, SHI Xingjian, and Dit-Yan Yeung. 2016. Collaborative recurrent autoencoder: Recommend while learning to fill in the blanks. In Advances in Neural Information Processing Systems. 415--423.

[23]

Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 153--162.

Digital Library

[24]

Yin Zheng, Cailiang Liu, Bangsheng Tang, and Hanning Zhou. 2016. Neural Autoregressive Collaborative Filtering for Implicit Feedback. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2--6.

Digital Library

Cited By

de Souza Pereira Moreira GFerreira Fda Cunha A(2018)News Session-Based Recommendations using Deep Neural NetworksProceedings of the 3rd Workshop on Deep Learning for Recommender Systems10.1145/3270323.3270328(15-23)Online publication date: 6-Oct-2018
https://dl.acm.org/doi/10.1145/3270323.3270328
Deldjoo YSchedl MHidasi BKnees PPera SEkstrand MAmatriain XO'Donovan J(2018)Multimedia recommender systemsProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3241620(537-538)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3241620
Hidasi BKaratzoglou ASar-Shalom OShapira BTikk DVasile FDieleman SPera SEkstrand MAmatriain XO'Donovan J(2018)DLRS 2018Proceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240333(512-513)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240333
Show More Cited By

Recommendations

RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS)
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

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 ...
DLRS 2018: third workshop on deep learning for recommender systems
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Deep learning is now an integral part of recommender systems, but the research is still in its early phase. New research topics pop up frequently and established topics are extended in new, interesting directions. DLRS 2018 is a venue for pioneering ...
Biocomputing 2017: Proceedings of the Pacific Symposium

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

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

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

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.

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

Check for updates

Author Tags

Qualifiers

Abstract

Conference

RecSys '17

Sponsor:

SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

Acceptance Rates

RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
400
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

de Souza Pereira Moreira GFerreira Fda Cunha A(2018)News Session-Based Recommendations using Deep Neural NetworksProceedings of the 3rd Workshop on Deep Learning for Recommender Systems10.1145/3270323.3270328(15-23)Online publication date: 6-Oct-2018
https://dl.acm.org/doi/10.1145/3270323.3270328
Deldjoo YSchedl MHidasi BKnees PPera SEkstrand MAmatriain XO'Donovan J(2018)Multimedia recommender systemsProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3241620(537-538)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3241620
Hidasi BKaratzoglou ASar-Shalom OShapira BTikk DVasile FDieleman SPera SEkstrand MAmatriain XO'Donovan J(2018)DLRS 2018Proceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240333(512-513)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240333
de Souza Pereira Moreira GPera SEkstrand MAmatriain XO'Donovan J(2018)CHAMELEONProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240331(578-583)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240331
Xu JHe XLi HCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Deep Learning for Matching in Search and RecommendationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210181(1365-1368)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210181

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