Enhancing a Collaborative Recommender System Based on Deep Learning for Online Resources / Manar Nedjai  

Public ISBD Titre : Enhancing a Collaborative Recommender System Based on Deep Learning for Online Resources Type de document : texte imprimé Auteurs : Manar Nedjai, Auteur ; Roumaissa Dana, Auteur ; Mediani,Chahrazed, Directeur de thèse Année de publication : 2023 Importance : 1 vol (59 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Collaborative filtering Recommender systems Index. décimale : 004 Informatique Résumé : Recommender Systems are software that can be used to filter out data from the volumes of data available online and provide recommendations to users in their area of interest. Recommender systems are classified into three types which are collaborative, content-based and hybrid. Among the proposed classifications, the collaborative filtering approach consists of finding the item that satisfies the user using other similar users evaluations. In recent years, deep neural networks have yielded immense success on many fields. However, some recent works have focused on combining deep learning with recommendation and have shown improvement in performance. In this thesis, we presented a novel model CFDAE to recommend online resources based on collaborative filtering using the denoising autoencoder, our model can perform good results in rating prediction of explicit feedback. Côte titre : MAI/0708 En ligne : https://drive.google.com/file/d/1gGj42ULJFZ11uwRcTO-gI1ySus9bQy8j/view?usp=drive [...] Format de la ressource électronique : pdf Enhancing a Collaborative Recommender System Based on Deep Learning for Online Resources [texte imprimé] / Manar Nedjai, Auteur ; Roumaissa Dana, Auteur ; Mediani,Chahrazed, Directeur de thèse . - 2023 . - 1 vol (59 f .) ; 29cm. Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Collaborative filtering Recommender systems Index. décimale : 004 Informatique Résumé : Recommender Systems are software that can be used to filter out data from the volumes of data available online and provide recommendations to users in their area of interest. Recommender systems are classified into three types which are collaborative, content-based and hybrid. Among the proposed classifications, the collaborative filtering approach consists of finding the item that satisfies the user using other similar users evaluations. In recent years, deep neural networks have yielded immense success on many fields. However, some recent works have focused on combining deep learning with recommendation and have shown improvement in performance. In this thesis, we presented a novel model CFDAE to recommend online resources based on collaborative filtering using the denoising autoencoder, our model can perform good results in rating prediction of explicit feedback. Côte titre : MAI/0708 En ligne : https://drive.google.com/file/d/1gGj42ULJFZ11uwRcTO-gI1ySus9bQy8j/view?usp=drive [...] Format de la ressource électronique : pdf

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0708	MAI/0708	Mémoire	Bibliothéque des sciences	Anglais	Disponible Disponible

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