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An Effective Recommender System Based on Clustering Technique for TED Talks

An Effective Recommender System Based on Clustering Technique for TED Talks

Faiz Maazouzi, Hafed Zarzour, Yaser Jararweh
Copyright: © 2020 |Volume: 15 |Issue: 1 |Pages: 17
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781799805274|DOI: 10.4018/IJITWE.2020010103
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MLA

Maazouzi, Faiz, et al. "An Effective Recommender System Based on Clustering Technique for TED Talks." IJITWE vol.15, no.1 2020: pp.35-51. https://doi.org/10.4018/IJITWE.2020010103

APA

Maazouzi, F., Zarzour, H., & Jararweh, Y. (2020). An Effective Recommender System Based on Clustering Technique for TED Talks. International Journal of Information Technology and Web Engineering (IJITWE), 15(1), 35-51. https://doi.org/10.4018/IJITWE.2020010103

Chicago

Maazouzi, Faiz, Hafed Zarzour, and Yaser Jararweh. "An Effective Recommender System Based on Clustering Technique for TED Talks," International Journal of Information Technology and Web Engineering (IJITWE) 15, no.1: 35-51. https://doi.org/10.4018/IJITWE.2020010103

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Abstract

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

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