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dc.contributor.authorAdjali, Naziha Fatma-
dc.contributor.authorAkrouche, Wassila-
dc.contributor.authorEl Bouhissi, Houda ; promotrice-
dc.date.accessioned2021-02-01T10:51:10Z-
dc.date.available2021-02-01T10:51:10Z-
dc.date.issued2020-
dc.identifier.urihttp://hdl.handle.net/123456789/14097-
dc.descriptionOption : software engineeringen_US
dc.description.abstractWith the increasing amount of data content produced daily, it becomes very di?cult for users to ?nd the resources suitable to their needs. Recommendation systems are proposed to solve this problem and are capable of providing personalized recommendations or guiding the user to interesting or useful resources within a large data space. Recently, Recommender systems are getting importance due to their signi?cance in making decisions and providing detailed information about the required product or a service. In this paper, we conduct a systematic review for recommendation models, and discuss the challenges and open issues. Furthermore, we propose a new recommendation system ontology-based in which machine-learning algorithms are used to achieve user needs identi?cation and provide precise recommendations.en_US
dc.language.isoenen_US
dc.publisheruniversité Abderrahmane Mira- Bejaiaen_US
dc.subjectMORES : Recommendation systems : Advantage : Typology : Semantic web : MLen_US
dc.titleMORES : A Movie Recommendation Systemen_US
dc.typeThesisen_US
Appears in Collections:Mémoires de Master

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