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A Novel Collaborative Filtering Approach Based on the Opposite Neighbors’ Preferences

TitreA Novel Collaborative Filtering Approach Based on the Opposite Neighbors’ Preferences
Publication TypeJournal Article
Year of Publication2020
AuthorsA. Fazziki, E, O. Aissaoui, E, Y. Alami, EMadani El, Benbrahim, M, Y. Allioui, E
JournalAdvances in Intelligent Systems and Computing
Volume1076
Pagination815-824
Mots-clésArtificial intelligence, Collaborative filtering, Embedded systems, Movielens, Recommendation techniques, Similar neighbors
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. © Springer Nature Singapore Pte Ltd. 2020.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085185804&doi=10.1007%2f978-981-15-0947-6_77&partnerID=40&md5=68fdeb907c6bb3635b6c19f73a225586
DOI10.1007/978-981-15-0947-6_77
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