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A customized non-exclusive clustering algorithm for news recommendation systems


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


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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