@article {Afoudi2021,
	title = {Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network},
	journal = {Simulation Modelling Practice and Theory},
	volume = {113},
	year = {2021},
	note = {cited By 14},
	abstract = {Recommendation systems are information filtering tools that present items to users based on their preferences and behavior, for example, suggestions about scientific papers or music a user might like. Based on what we said and with the development of computer science that has started to take an interest in big data and how it is used to discover user interest, we have found a lot of research going on in the area of recommendation and there are powerful systems available. In the unsupervised learning domain, this paper introduces a novel method for creating a hybrid recommender framework that combines Collaborative Filtering with Content Based Approach and Self-Organizing Map neural network technique. By testing our system on a subset of the Movies Database, we demonstrate that our method outperforms state-of-the-art methods in terms of accuracy and precision, as well as improving the efficiency of the traditional Collaborative Filtering methodology. {\textcopyright} 2021 Elsevier B.V.},
	keywords = {Accuracy and precision, Collaborative filtering, Collaborative predictions, Conformal mapping, Content based filtering, Content-based approach, Hybrid recommendation, Recommender Systems, Scientific papers, Self organizing maps, Self-organizing map neural network, State-of-the-art methods, Well testing},
	doi = {10.1016/j.simpat.2021.102375},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112615681\&doi=10.1016\%2fj.simpat.2021.102375\&partnerID=40\&md5=6a08638f8df46e823320bf446b6eb978},
	author = {Afoudi, Y. and Lazaar, M. and Al Achhab, M.}
}
