RecSys 16 workshop on deep learning for recommender systems (DLRS)

Alexandros Karatzoglou; Balázs Hidasi; Domonkos Tikk; Oren Sar-Shalom; Haggai Roitman; Bracha Shapira

doi:10.1145/2959100.2959202

RecSys 16 workshop on deep learning for recommender systems (DLRS)

Alexandros Karatzoglou, Balázs Hidasi, Domonkos Tikk, Oren Sar-Shalom, Haggai Roitman, Bracha Shapira

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Scopus citations

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, nat- ural language processing and speech recognition. Despite this, only little work has been published on Deep Learning methods for Recommender Systems. Notable recent appli- cation areas are music recommendation, news recommenda- tion, 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.

Original language	English
Title of host publication	RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems
Publisher	Association for Computing Machinery, Inc
Pages	415-416
Number of pages	2
ISBN (Electronic)	9781450340359
DOIs	https://doi.org/10.1145/2959100.2959202
State	Published - 7 Sep 2016
Externally published	Yes
Event	10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States Duration: 15 Sep 2016 → 19 Sep 2016

Publication series

Name	RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems

Conference

Conference	10th ACM Conference on Recommender Systems, RecSys 2016
Country/Territory	United States
City	Boston
Period	15/09/16 → 19/09/16

Bibliographical note

Publisher Copyright:
© 2016 Copyright held by the owner/author(s).

Keywords

Deep learning
Neural networks
Recommender systems

Access to Document

10.1145/2959100.2959202

Cite this

Karatzoglou, A., Hidasi, B., Tikk, D., Sar-Shalom, O., Roitman, H., & Shapira, B. (2016). RecSys 16 workshop on deep learning for recommender systems (DLRS). In RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems (pp. 415-416). (RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems). Association for Computing Machinery, Inc. https://doi.org/10.1145/2959100.2959202

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Karatzoglou, A, Hidasi, B, Tikk, D, Sar-Shalom, O, Roitman, H & Shapira, B 2016, RecSys 16 workshop on deep learning for recommender systems (DLRS). in RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems. RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, Association for Computing Machinery, Inc, pp. 415-416, 10th ACM Conference on Recommender Systems, RecSys 2016, Boston, United States, 15/09/16. https://doi.org/10.1145/2959100.2959202

RecSys 16 workshop on deep learning for recommender systems (DLRS). / Karatzoglou, Alexandros; Hidasi, Balázs; Tikk, Domonkos et al.
RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc, 2016. p. 415-416 (RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Karatzoglou A, Hidasi B, Tikk D, Sar-Shalom O, Roitman H, Shapira B. RecSys 16 workshop on deep learning for recommender systems (DLRS). In RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc. 2016. p. 415-416. (RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems). doi: 10.1145/2959100.2959202

RecSys 16 workshop on deep learning for recommender systems (DLRS)

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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