research-article

Deep Learning Based-Recommendation System: An Overview on Models, Datasets, Evaluation Metrics, and Future Trends

Authors:

Kok-Why NgAuthors Info & Claims

CIIS '19: Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems

Pages 6 - 11

https://doi.org/10.1145/3372422.3372444

Published: 07 February 2020 Publication History

Abstract

The growth of data in recent years has motivated the emergence of deep learning in many Computer Sciences related fields including Recommender System (RS). Deep learning has emerged as the solution; overcoming the obstacles of traditional recommendation models. Deep learning is able to enhance recommendation quality by learning non-linear and non-trivial user-item relationship, and extracting deep and abstract feature representations for users and items. However, deep learning in RS is still new and flourishing. The contribution of this paper is two-folds. Firstly, we will be providing several insights on the advances of RS focusing on deep-learning models, datasets and evaluation metrics. Secondly, we expand on the current trend and provide several possible research directions in the field of deep learning-based RS.

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Cited By

Yang YWoradit KCosh K(2025)Hybrid Movie Recommendation System With User Partitioning and Log Likelihood Content ComparisonIEEE Access10.1109/ACCESS.2025.352951513(11609-11622)Online publication date: 2025
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Maghsoudi MHossein Valikhani MHossein Zohdi M(2025)Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated FrameworkIEEE Access10.1109/ACCESS.2025.352687613(5660-5678)Online publication date: 2025
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Kim YChang T(2024)Deep Learning-Based Freight Recommendation System for Freight Brokerage PlatformSystems10.3390/systems1211047712:11(477)Online publication date: 7-Nov-2024
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Index Terms

Deep Learning Based-Recommendation System: An Overview on Models, Datasets, Evaluation Metrics, and Future Trends
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Other conferences

CIIS '19: Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems

November 2019

200 pages

ISBN:9781450372596

DOI:10.1145/3372422

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Queensland University of Technology
City University of Hong Kong: City University of Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2020

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CIIS 2019: 2019 The 2nd International Conference on Computational Intelligence and Intelligent Systems

November 23 - 25, 2019

Bangkok, Thailand

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Cited By

Yang YWoradit KCosh K(2025)Hybrid Movie Recommendation System With User Partitioning and Log Likelihood Content ComparisonIEEE Access10.1109/ACCESS.2025.352951513(11609-11622)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3529515
Maghsoudi MHossein Valikhani MHossein Zohdi M(2025)Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated FrameworkIEEE Access10.1109/ACCESS.2025.352687613(5660-5678)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3526876
Kim YChang T(2024)Deep Learning-Based Freight Recommendation System for Freight Brokerage PlatformSystems10.3390/systems1211047712:11(477)Online publication date: 7-Nov-2024
https://doi.org/10.3390/systems12110477
Wen YTi P(2024)A Study of Legal Judgment Prediction Based on Deep Learning Multi-Fusion Models—Data from ChinaSage Open10.1177/2158244024125768214:3Online publication date: 9-Sep-2024
https://doi.org/10.1177/21582440241257682
El-Shaer EMcKee GHamdy A(2024)The LSTM-EMPG Model for Next Basket Recommendation in E-commerceInternational Journal of Information and Communication Sciences10.11648/j.ijics.20240901.129:1(9-23)Online publication date: 15-Jul-2024
https://doi.org/10.11648/j.ijics.20240901.12
Raghuraman SB AD KR KDeepan P S(2024)Design and Development of Artificial General Intelligence for Power System Operation2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)10.1109/ICPECTS62210.2024.10780272(1-6)Online publication date: 8-Oct-2024
https://doi.org/10.1109/ICPECTS62210.2024.10780272
Mohamedhen AAlfazi AArfaoui NEjbali RNanne M(2024)Towards Multi-Agent System for Learning Object RecommendationHeliyon10.1016/j.heliyon.2024.e39088(e39088)Online publication date: Oct-2024
https://doi.org/10.1016/j.heliyon.2024.e39088
Marcuzzo HPereira MAlves PFoleis J(2024)AquaVitae: Innovating Personalized Meal Recommendations for Enhanced Nutritional HealthOptimization, Learning Algorithms and Applications10.1007/978-3-031-53025-8_11(148-161)Online publication date: 1-Feb-2024
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Hussain SSonghua XAslam MHussain F(2023)Clinical predictions of COVID-19 patients using deep stacking neural networksJournal of Investigative Medicine10.1177/1081558923120110372:1(112-127)Online publication date: 10-Nov-2023
https://doi.org/10.1177/10815589231201103
Bali NSingh KDhawan S(2023)A Survey and Comparative Analysis of Relevant Approaches of Recommendation System2023 6th International Conference on Contemporary Computing and Informatics (IC3I)10.1109/IC3I59117.2023.10397781(750-755)Online publication date: 14-Sep-2023
https://doi.org/10.1109/IC3I59117.2023.10397781
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