TOUATI Yanis2025-01-212025-01-212025-01-21https://dspace.estin.dz/handle/123456789/26This dissertation presents a multi-model hybrid recommendation approach aimed at enhancing recommenda tion systems’ performance. The approach integrates content-based recommendation, collaborative filtering, and clustering techniques to exploit the strengths of each method. We discuss the architecture and moti vation behind our approach, along with its objectives, expectations, limitations, and potential challenges. The proposed model incorporates visual data processing through deep feature extraction techniques, as well as textual-based models utilizing word and sequence embedding learning. We also detail collaborative fil tering recommendation using similarity metrics, alongside clustering techniques including deep contrastive clustering. for further enhancement, we explore the hybridization of these methods to further improve rec ommendation accuracy. Experimental results and discussions are also displayed at the end of our paper, offering insights for future research in recommendation system development.enArtficial IntelligenceCollaborative FilteringVisual Data IntegrationRecommender Sys temsContent-Based RecommendationFeature ExtractThesis