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.
Highlights
Clustering is one of the main tasks in machine learning and data mining that has been widely applied in the field of time series prediction, recommendation, and parameter estimation [1,2]
In this paper an efficient clustering algorithm named Ordered Clustering (OC) is proposed for news clustering based on the news nature in which a news item can belong to more than one category with the aim to achieve accurate and diverse recommendations
We proposed a new non-exclusive clustering algorithm named Ordered Clustering (OC) that is dedicated to news recommendation
Summary
Clustering is one of the main tasks in machine learning and data mining that has been widely applied in the field of time series prediction, recommendation, and parameter estimation [1,2]. The most commonly used clustering algorithms in recommendation systems are hierarchical clustering [5,7] and k-means [8,9]. These clustering algorithms do not take into consideration the news nature in clustering the news items. In this paper an efficient clustering algorithm named Ordered Clustering (OC) is proposed for news clustering based on the news nature in which a news item can belong to more than one category with the aim to achieve accurate and diverse recommendations. This is followed by description of the proposed clustering algorithm. The experimental evaluations are presented which is followed by a summary of this research work
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