Intelligence Artificielle et Data Sciences

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Browsing Intelligence Artificielle et Data Sciences by Author "TOUATI Yanis"

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    Exploring the Integration of Visual Data for Enhancing the Accuracy and Reliability of Recommender Systems
    (Tassadit, 2025-01-21) TOUATI Yanis
    This 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.
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    State-of-the-Art Analysis : Exploring the Integration of Visual Data for Enhancing the Accuracy and Reliability of Recommender Systems
    (Tassadit, 2025-01-25) TOUATI Yanis
    This dissertation presents a comprehensive analysis of recommender systems, focusing on the integration of visual data to enhance recommendation accuracy and reliability. Chapter One provides a General introduction to recommender systems, emphasizing their importance in our nowadays technologies. Chapter Two explores the state-of-the-art in recommender systems, discussing various methodologies and advancements, with a particular focus on the incorporation of visual data. The chapter highlights the transformative potential of visual data integration in shaping personalized recommendation experiences across different platforms and domains and then we conclude the study by synthesizing the the summarizing of the State-of-the-Art and the challenges associated with visual data integration.