@article {Yassine2021,
	title = {Intelligent recommender system based on unsupervised machine learning and demographic attributes},
	journal = {Simulation Modelling Practice and Theory},
	volume = {107},
	year = {2021},
	note = {cited By 22},
	abstract = {Recommendation systems aim to predict users interests and recommend items most likely to interest them. In this paper, we propose a new intelligent recommender system that combines collaborative filtering (CF) with the popular unsupervised machine learning algorithm K-means clustering. Also, we use certain user demographic attributes such as the gender and age to create segmented user profiles, when items (movies) are clustered by genre attributes using K-means and users are classified based on the preference of items and the genres they prefer to watch. To recommend items to an active user, Collaborative Filtering approach then is applied to the cluster where the user belongs. Following the experimentation for well known movies, we show that the proposed system satisfies the predictability of the CF algorithm in GroupLens. In addition, our proposed system improves the performance and time response speed of the traditional collaborative Filtering technique and the Content-Based technique too. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {Collaborative filtering, Collaborative filtering techniques, Content-based techniques, Intelligent recommender system, K-means, K-means clustering, Learning algorithms, Machine learning, Most likely, Population statistics, Recommender Systems, Response speed, Unsupervised machine learning, User profile},
	doi = {10.1016/j.simpat.2020.102198},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096665603\&doi=10.1016\%2fj.simpat.2020.102198\&partnerID=40\&md5=37861e6691cce74784d0555379aae57f},
	author = {Yassine, A. and Mohamed, L. and Al Achhab, M.}
}
