A Customized Non-Exclusive Clustering Algorithm for News Recommendation Systems
Asghar Darvishy, Hamidah Ibrahim, Fatimah Sidi, Aida Mustapha · Journal of University of Babylon for Pure and Applied Sciences · 2019
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.