A Deep Learning Model For Collaborative Filtering / Ferkous, Mohamed Laid  

Public ISBD Titre : A Deep Learning Model For Collaborative Filtering Type de document : texte imprimé Auteurs : Ferkous, Mohamed Laid, Auteur ; Ahlem Drif, Directeur de thèse Editeur : Setif:UFA Année de publication : 2021 Importance : 1 vol (83 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Recommender systems Mutual Influence Graph Attention Network Index. décimale : 004 - Informatique Résumé : Recommender systems have become an integral part of e-commerce sites and other platforms such as social networking, movie/-music rendering websites. Network representation learning have been used as a successful approaches to build efficient recommender systems. However, learning the mutual influence generated by the contributions of each node in the network is a challenging issue. In fact, the mutual influence carries collaborative signals on user decisions helping to account for complex user-item interactions. For this purpose, in this master thesis, we propose Multual Intercation Graph Attention Network ‘MIGAN” which is a new algorithm based on a the self-supervised representation learning on large scale bipartite graph (BGNN). Experiments on real-world datasets demonstrate that the proposed Graph learning method can achieve more accurate predictions and higher recommendation efficiency. Côte titre : MAI/0522 En ligne : https://drive.google.com/file/d/1mGYDn5WsQu4tz8wXwWqFf3RXOFw_iyJV/view?usp=shari [...] Format de la ressource électronique : pdf A Deep Learning Model For Collaborative Filtering [texte imprimé] / Ferkous, Mohamed Laid, Auteur ; Ahlem Drif, Directeur de thèse . - [S.l.] : Setif:UFA, 2021 . - 1 vol (83 f .) ; 29 cm. Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Recommender systems Mutual Influence Graph Attention Network Index. décimale : 004 - Informatique Résumé : Recommender systems have become an integral part of e-commerce sites and other platforms such as social networking, movie/-music rendering websites. Network representation learning have been used as a successful approaches to build efficient recommender systems. However, learning the mutual influence generated by the contributions of each node in the network is a challenging issue. In fact, the mutual influence carries collaborative signals on user decisions helping to account for complex user-item interactions. For this purpose, in this master thesis, we propose Multual Intercation Graph Attention Network ‘MIGAN” which is a new algorithm based on a the self-supervised representation learning on large scale bipartite graph (BGNN). Experiments on real-world datasets demonstrate that the proposed Graph learning method can achieve more accurate predictions and higher recommendation efficiency. Côte titre : MAI/0522 En ligne : https://drive.google.com/file/d/1mGYDn5WsQu4tz8wXwWqFf3RXOFw_iyJV/view?usp=shari [...] Format de la ressource électronique : pdf

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0522	MAI/0522	Mémoire	Bibliothéque des sciences	Anglais	Disponible Disponible

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