Titre :	Enhancing a Content-Based Recommender System using Deep Learning for Online Resources
Type de document :	texte imprimé
Auteurs :	hanane Bouaziz, Auteur ; meriem Deghmoum, Auteur ; Mediani,Chahrazed, Directeur de thèse
Année de publication :	2023
Importance :	1 vol (52 f .)
Format :	29cm
Langues :	Français (fre)
Catégories :	Thèses & Mémoires:Informatique
Mots-clés :	Recommender system content-based
Index. décimale :	004 Informatique
Résumé :	In light of the expansion of digital content and the escalating need for personalized recommendations, recommender systems have emerged as essential tools in facilitating user exploration and involvement. Among the different approaches available, content-based recommender systems utilize the inherent attributes of items to produce recommendations. in This thesis we present the design and implementation of a deep learning-based content-based recommender system. We explore different deep learning architectures, including TF-IDF,Word2Vec, TF-IDF with Logistic Regression,Word2Vec with Logistic Regression, and LSTM models, to capture item and user preferences from the available data. These models are evaluated using precision, recall, and F1-score metrics to assess their performance in recommending relevant items. with the goal of improving the quality and performance of such systems.
Côte titre :	MAI/0705
En ligne :	https://drive.google.com/file/d/1LmOPrce-ApSpOeoLja6XBN8Rcb-k-uAH/view?usp=drive [...]
Format de la ressource électronique :	pdf

Enhancing a Content-Based Recommender System using Deep Learning for Online Resources [texte imprimé] / hanane Bouaziz, Auteur ; meriem Deghmoum, Auteur ; Mediani,Chahrazed, Directeur de thèse . - 2023 . - 1 vol (52 f .) ; 29cm.
Langues : Français (fre)

Catégories :	Thèses & Mémoires:Informatique
Mots-clés :	Recommender system content-based
Index. décimale :	004 Informatique
Résumé :	In light of the expansion of digital content and the escalating need for personalized recommendations, recommender systems have emerged as essential tools in facilitating user exploration and involvement. Among the different approaches available, content-based recommender systems utilize the inherent attributes of items to produce recommendations. in This thesis we present the design and implementation of a deep learning-based content-based recommender system. We explore different deep learning architectures, including TF-IDF,Word2Vec, TF-IDF with Logistic Regression,Word2Vec with Logistic Regression, and LSTM models, to capture item and user preferences from the available data. These models are evaluated using precision, recall, and F1-score metrics to assess their performance in recommending relevant items. with the goal of improving the quality and performance of such systems.
Côte titre :	MAI/0705
En ligne :	https://drive.google.com/file/d/1LmOPrce-ApSpOeoLja6XBN8Rcb-k-uAH/view?usp=drive [...]
Format de la ressource électronique :	pdf