Citation

Haw, Su Cheng and Chew, Lit Jiew and Ng, Kok Why and Naveen, Palanichamy and Gandhi, Arfive and Martha, Ati Suci Dian (2022) Ontology-based Recommender System with Descriptive Analytics in e-Commerce. In: 2022 2nd International Conference on Big Data Engineering and Education (BDEE), 5-7 Aug 2022, Chengdu, China.

Text 44.pdf - Published Version Restricted to Repository staff only Download (636kB)

Abstract

The amount of information and users has been increasing at a remarkable rate in recent years. This is where the recommender system comes in, recommender system is a system that generates a list of recommended products for the user. Recommender system has outshined as one of the important features in an e-Commerce portal. Several recommender techniques have been proposed, yet, problems such as cold-start item problem, cold-start user problem and data sparsity problem still existed. The expected outcomes of this paper are an ontology-based recommender system, combining the collaborative and content-based filtering approaches. In addition, the recommender system also generates recommendations by considering user preferences based on social network association. The experimental evaluation indicated that the proposed approach works more efficiently and effectively improves the recommendation.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Content-based , recommender system , recommender technique , ontology , e-commerce
Subjects:	H Social Sciences > HF Commerce > HF5001-6182 Business > HF5546-5548.6 Office management
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	15 Mar 2023 04:17
Last Modified:	15 Mar 2023 04:17
URII:	http://shdl.mmu.edu.my/id/eprint/11230

Downloads

Edit (login required)