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Semantic Web mining for Content-Based Online Shopping Recommender Systems

Afolabi, I. T. and makinde, opeyemi and Oladipupo, O. O. (2021) Semantic Web mining for Content-Based Online Shopping Recommender Systems. International Journal of Intelligent Information Technologies, 15 (4). ISSN 1548-3657 EISSN: 1548-3665

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Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

Item Type: Article
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Dr ibukun Afolabi
Date Deposited: 21 Jun 2021 16:55
Last Modified: 21 Jun 2021 16:55

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