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A customized non-exclusive clustering algorithm for news recommendation systems


Citation

Ibrahim, Hamidah and Sidi, Fatimah and Mustapha, Aida and Darvishy, Asghar (2019) A customized non-exclusive clustering algorithm for news recommendation systems. Journal of University of Babylon, Pure and Applied Sciences (JUBES), 27 (1). pp. 368-379. ISSN 1992-0652; ESSN: 2312-8135

Abstract

Clustering is one of the main tasks in machine learning and data mining and is being utilized in many applications including news recommendation systems. In this paper, we propose a new non-exclusive clustering algorithm named Ordered Clustering (OC) with the aim is to increase the accuracy of news recommendation for online users. The basis of OC is a new initialization technique that groups news items into clusters based on the highest similarities between news items to accommodate news nature in which a news item can belong to different categories. Hence, in OC, multiple memberships in clusters are allowed. An experiment is carried out using a real dataset which is collected from the news websites. The experimental results demonstrated that the OC outperforms the k-means algorithm with respect to Precision, Recall, and F1-Score.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.29196/jubpas.v27i1.2192
Publisher: University of Babylon
Keywords: Clustering algorithm; Non-exclusive clustering; News recommendation; Similarity weight
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 06 Nov 2020 19:03
Last Modified: 06 Nov 2020 19:03
Altmetrics: http://www.altmeric.com/details.php?domain=psasir.upm.edu.my&doi=10.29196/jubpas.v27i1.2192
URI: http://psasir.upm.edu.my/id/eprint/80406
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