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DC Field | Value | Language |
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dc.contributor.author | Adjali, Naziha Fatma | - |
dc.contributor.author | Akrouche, Wassila | - |
dc.contributor.author | El Bouhissi, Houda ; promotrice | - |
dc.date.accessioned | 2021-02-01T10:51:10Z | - |
dc.date.available | 2021-02-01T10:51:10Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://hdl.handle.net/123456789/14097 | - |
dc.description | Option : software engineering | en_US |
dc.description.abstract | With 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.iso | en | en_US |
dc.publisher | université Abderrahmane Mira- Bejaia | en_US |
dc.subject | MORES : Recommendation systems : Advantage : Typology : Semantic web : ML | en_US |
dc.title | MORES : A Movie Recommendation System | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Mémoires de Master |
Files in This Item:
File | Description | Size | Format | |
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MemoireFCorrige_akrouche_adjali_09.2020.pdf | 7.33 MB | Adobe PDF | View/Open |
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