Citation

Lim, Ying Fei and Haw, Su Cheng and Ng, Kok Why and Jayaram, Jayapradha (2025) Hybrid-Based Recommender System Utilizing Ontology for Semantic Modeling in E-Commerce. TEM Journal, 14 (1). pp. 2196-2207. ISSN 2217-8309

Text Hybrid-Based Recommender System Utilizing Ontology for Semantic Modeling in E-Commerce.pdf - Published Version Restricted to Repository staff only Download (744kB)

Abstract

Recommendation algorithms improve the functionality of online shopping platforms by assisting buyers in selecting the best products depending on their interests. In essence, recommender engines are a component of an online personalized strategy that enhances user experience via dynamically loading different types of content into emails, apps, and websites. This paper focuses on investigating and putting into practice data filtering techniques that are commonly used in recommender systems. These techniques include semantic-based filtering, hybrid filtering, collaborative filtering, content-based filtering, graph-based filtering and ontology-based filtering. In order to understand these methods and how they operate, a brief discussion of the relevant state-of-theart research will be conducted. Next, this paper will then dive deeper into various semantic-based recommendation techniques to build an ontology for modeling semantic data and relationships. Ontology is simple to expand because the relationships and concept can easily be matched and added to the existing corpus. Furthermore, ontology also offers the ability to represent all data types, including structured, unstructured, or semi-structured, thus, facilitating more seamless data integration.

Item Type:	Article
Uncontrolled Keywords:	E-commerce, hybrid-based ontology, , recommender system, semantic modelling
Subjects:	H Social Sciences > HF Commerce > HF5001-6182 Business > HF5546-5548.6 Office management > HF5548.32-.34 Electronic commerce
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	07 Nov 2025 07:21
Last Modified:	07 Nov 2025 07:21
URII:	http://shdl.mmu.edu.my/id/eprint/14782

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