A customized non-exclusive clustering algorithm for news recommendation systems
Joint Authors
Ibrahim, Hamidah
Sidi, Fatimah
Mustafa, Ayidah
Darwishi, Asghar
Source
Journal of Babylon University : Journal of Applied and Pure Sciences
Issue
Vol. 27, Issue 1 (28 Feb. 2019), pp.368-379, 12 p.
Publisher
Publication Date
2019-02-28
Country of Publication
Iraq
No. of Pages
12
Main Subjects
Abstract EN
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 tech- nique 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 membership in clusters is 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.
American Psychological Association (APA)
Darwishi, Asghar& Ibrahim, Hamidah& Sidi, Fatimah& Mustafa, Ayidah. 2019. A customized non-exclusive clustering algorithm for news recommendation systems. Journal of Babylon University : Journal of Applied and Pure Sciences،Vol. 27, no. 1, pp.368-379.
https://search.emarefa.net/detail/BIM-1094609
Modern Language Association (MLA)
Darwishi, Asghar…[et al.]. A customized non-exclusive clustering algorithm for news recommendation systems. Journal of Babylon University : Journal of Applied and Pure Sciences Vol. 27, no. 1 (2019), pp.368-379.
https://search.emarefa.net/detail/BIM-1094609
American Medical Association (AMA)
Darwishi, Asghar& Ibrahim, Hamidah& Sidi, Fatimah& Mustafa, Ayidah. A customized non-exclusive clustering algorithm for news recommendation systems. Journal of Babylon University : Journal of Applied and Pure Sciences. 2019. Vol. 27, no. 1, pp.368-379.
https://search.emarefa.net/detail/BIM-1094609
Data Type
Journal Articles
Language
English
Record ID
BIM-1094609