MAKINDE, OPEYEMI SAMUEL and Covenant University, Theses (2018) SEMANTIC WEB CONTENT MINING FOR CONTENT-BASED RECOMMENDER SYSTEMS. Masters thesis, COVENANT UNIVERSITY.
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Abstract
The fast-growing presence of data is crucial to all sectors and domain as it is being harnessed to solve various real-time problems, such as product recommendation. Web content mining, which is referred to a data mining for web textual content can be used to retrieve, refine and analyze data to solve these problems. It is therefore important that the web content mining process is optimized to improve preprocessing of web textual data for efficient recommendation. 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. The methodology is based on two major phases. The first phase is the semantic preprocessing of data. This phase uses both a developed ontology and an existing ontology together with the typical text preprocessing steps such as filtration stemming and so on. 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 which provides an edge over the existing recommender approach because it is able to analyze the textual contents of users feedback on a product.
| Item Type: | Thesis (Masters) |
|---|---|
| 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: | Mrs Hannah Akinwumi |
| Date Deposited: | 25 Mar 2020 08:57 |
| Last Modified: | 25 Mar 2020 08:57 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/13264 |
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