Citation

Ong, Kyle and Haw, Su Cheng and Ng, Kok Why (2019) Deep Learning Based-Recommendation System: An Overview on Models, Datasets, Evaluation Metrics, and Future Trends. In: 2nd International Conference on Computational Intelligence and Intelligent Systems, 23-25 Nov. 2019, AVANI Atrium Bangkok, Bangkok, Thailand.

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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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Deep learning, deep learning model, recommender system, hybrid-based, evaluation metrics
Subjects:	L Education > LB Theory and practice of education > LB1025-1050.75 Teaching (Principles and practice)
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	15 Oct 2021 06:48
Last Modified:	15 Oct 2021 06:48
URII:	http://shdl.mmu.edu.my/id/eprint/9557

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