@article {ElFazziki2020136,
	title = {Improving Collaborative Filtering Approach by Leveraging Opposite Users},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1102 AISC},
	year = {2020},
	note = {cited By 0},
	pages = {136-145},
	abstract = {Collaborative filtering is a widely used recommendation approach that aims to predict for a target user the most appropriate items. This approach uses the ratings given by users who share similar tastes and preferences to predict ratings for items that haven{\textquoteright}t been rated yet. Despite its simplicity and justifiability, CF approach stills suffering from several drawbacks and problems, including sparsity, gray sheep and scalability. These problems affect the accuracy of the obtained results. In this work, we present a novel collaborative filtering approach based on the opposite preferences of users. We focus on enhancing the accuracy of predictions and dealing with gray sheep problem by inferring new similar neighbors based on users who have dissimilar tastes and preferences. For instance, if a user X is dissimilar to a user Y then the user +X is similar to the user Y. The Experimental results performed on two datasets including MovieLens and FilmTrust show that our approach outperforms several baseline recommendation techniques. {\textcopyright} Springer Nature Switzerland AG 2020.},
	keywords = {Collaborative filtering, Forecasting, Gray sheep, Intelligent systems, Learning systems, Movielens, Opposite neighbors, planning, Recommendation techniques, Recommender Systems, Similar neighbors, Similarity measure, Sustainable development},
	doi = {10.1007/978-3-030-36653-7_14},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078457465\&doi=10.1007\%2f978-3-030-36653-7_14\&partnerID=40\&md5=fdeb5a86480517f6637d45ebc49b0849},
	author = {El Fazziki, A. and El Madani El Alami, Y. and El Aissaoui, O. and El Allioui, Y. and Benbrahim, M.}
}
@article {ElFazziki2020815,
	title = {A Novel Collaborative Filtering Approach Based on the Opposite Neighbors{\textquoteright} Preferences},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1076},
	year = {2020},
	note = {cited By 0},
	pages = {815-824},
	abstract = {Collaborative filtering (CF) has become an effective way to predict useful items. It is the most widespread recommendation technique. It relies on users who share similar tastes and preferences to suggest the items that they might be interested in. Despite its simplicity and justifiability, the collaborative filtering approach experiences many problems, including sparsity, gray sheep and scalability. These problems lead to deteriorating the accuracy of the obtained results. In this work, we present a novel collaborative filtering approach based on the opposite preferences of users. We focus on enhancing the accuracy of predictions and dealing with gray sheep problem by inferring new similar neighbors based on users who have dissimilar tastes and preferences. For instance, if a user X is dissimilar to a user Y then the user +X is similar to the user Y. The Experimental results performed on two datasets including MovieLens and FilmTrust show that our approach outperforms several baseline recommendation techniques. {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.},
	keywords = {Artificial intelligence, Collaborative filtering, Embedded systems, Movielens, Recommendation techniques, Similar neighbors},
	doi = {10.1007/978-981-15-0947-6_77},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085185804\&doi=10.1007\%2f978-981-15-0947-6_77\&partnerID=40\&md5=68fdeb907c6bb3635b6c19f73a225586},
	author = {El Fazziki, A. and El Aissaoui, O. and El Madani El Alami, Y. and Benbrahim, M. and El Allioui, Y.}
}
