tutorial

Deep Learning for Recommender Systems

Authors:

Alexandros Karatzoglou,

Balázs HidasiAuthors Info & Claims

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

Pages 396 - 397

https://doi.org/10.1145/3109859.3109933

Published: 27 August 2017 Publication History

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Abstract

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 processing and speech recognition. After its relatively slow uptake by the recommender systems community, deep learning for recommender systems became widely popular in 2016.

We believe that a tutorial on the topic of deep learning will do its share to further popularize the topic. Notable recent application areas are music recommendation, news recommendation, and session-based recommendation. The aim of the tutorial is to encourage the application of Deep Learning techniques in Recommender Systems, to further promote research in deep learning methods for Recommender Systems.

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Cited By

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Kichou SIsmail BAmine E(2024)Deep Learning-Based cloud services recommendation2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS)10.1109/PAIS62114.2024.10541124(1-8)Online publication date: 24-Apr-2024
https://doi.org/10.1109/PAIS62114.2024.10541124
Lin TNatsupakpong SKhanarsa P(2024)Deep Learning-Based Course Recommendations Using Sentence Embeddings and User Information for Learning Platforms2024 28th International Computer Science and Engineering Conference (ICSEC)10.1109/ICSEC62781.2024.10770749(1-6)Online publication date: 6-Nov-2024
https://doi.org/10.1109/ICSEC62781.2024.10770749
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Index Terms

Deep Learning for Recommender Systems
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
2. Information systems
  1. Information retrieval

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 ...
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 ...
Exploiting deep transformer models in textual review based recommender systems
Abstract
Textual reviews contain fine-grained information that can effectively infer user preferences over the items. Accordingly, the latest studies in the field of recommender systems exploit content-rich review texts to complement user and item ...

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Information & Contributors

Information

Published In

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

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

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

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

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View all

Nagula PAlexakis C(2025)Forecasting Natural Gas Futures Prices Using Hybrid Machine Learning Models During Turbulent Market Conditions: The Case of the Russian–Ukraine CrisisJournal of Forecasting10.1002/for.3250Online publication date: 5-Jan-2025
https://doi.org/10.1002/for.3250
Kichou SIsmail BAmine E(2024)Deep Learning-Based cloud services recommendation2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS)10.1109/PAIS62114.2024.10541124(1-8)Online publication date: 24-Apr-2024
https://doi.org/10.1109/PAIS62114.2024.10541124
Lin TNatsupakpong SKhanarsa P(2024)Deep Learning-Based Course Recommendations Using Sentence Embeddings and User Information for Learning Platforms2024 28th International Computer Science and Engineering Conference (ICSEC)10.1109/ICSEC62781.2024.10770749(1-6)Online publication date: 6-Nov-2024
https://doi.org/10.1109/ICSEC62781.2024.10770749
Zhong HWu C(2024)T-UNet: triplet UNet for change detection in high-resolution remote sensing imagesGeo-spatial Information Science10.1080/10095020.2024.2338224(1-18)Online publication date: 17-Apr-2024
https://doi.org/10.1080/10095020.2024.2338224
Byeon HChunduri VNarang GAlghayadh FSoni MRamesh J(2024)Deep learning model for recommendation system using web of things based knowledge graph miningService Oriented Computing and Applications10.1007/s11761-024-00409-8Online publication date: 26-May-2024
https://doi.org/10.1007/s11761-024-00409-8
Zhang LZhang YCao XLiu S(2024)Graph-based dynamic attribute clipping for conversational recommendationDiscover Computing10.1007/s10791-024-09437-627:1Online publication date: 10-May-2024
https://doi.org/10.1007/s10791-024-09437-6
Alam MAhmed M(2024)Deep Learning-Based Recommendation Systems: Review and Critical AnalysisProceedings of Data Analytics and Management10.1007/978-981-99-6544-1_4(39-55)Online publication date: 14-Jan-2024
https://doi.org/10.1007/978-981-99-6544-1_4
Bashir SRaza SMisic V(2023)BERT4Loc: BERT for Location—POI Recommender SystemFuture Internet10.3390/fi1506021315:6(213)Online publication date: 12-Jun-2023
https://doi.org/10.3390/fi15060213
Wang XDong H(2023)Click-through Rate Prediction and Uncertainty Quantification Based on Bayesian Deep LearningEntropy10.3390/e2503040625:3(406)Online publication date: 23-Feb-2023
https://doi.org/10.3390/e25030406
Unger MLi PSen STuzhilin A(2023)Don’t Need All Eggs in One Basket: Reconstructing Composite Embeddings of Customers from Individual-Domain EmbeddingsACM Transactions on Management Information Systems10.1145/357871014:2(1-30)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3578710
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Recommendations

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

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

Exploiting deep transformer models in textual review based recommender systems

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