Citation

Lim, Ying Fei and Haw, Su Cheng and Ng, Kok Why and Anaam, Elham Abdulwahab (2023) Hybrid-based Recommender System for Online Shopping: A Review. Journal of Engineering Technology and Applied Physics, 5 (1). pp. 12-34. ISSN 2682-8383

Text (Hybrid-based Recommender System for Online Shopping: A Review) 348 - Published Version Restricted to Repository staff only Download (2kB)

Abstract

In the era of the digital revolution, online shopping has developed into a remarkably simple and economical option for consumers to make purchases securely and conveniently from their homes. In order for the online merchant to optimize their profit, the online shopping platform must always display a list of potential products that customers may purchase. The recommender system kicks in at this point to assist in finding products that customers would like and recommend a list of product recommendations that match the customer's preferences. This paper reviews the recommender system technology in detail by reviewing the classification technique. Other than that, the related works will be reviewed to understand how each technique works, the strengths and limitations, the datasets and evaluation metrics employed.

Item Type:	Article
Uncontrolled Keywords:	Recommender System, Online shopping, Semantic, Hybrid-based, Descriptive analytics
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	19 Aug 2024 07:40
Last Modified:	19 Aug 2024 07:40
URII:	http://shdl.mmu.edu.my/id/eprint/12823

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