University Sétif 1 FERHAT ABBAS Faculty of Sciences
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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 NetworkIndex. 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 : 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 NetworkIndex. 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 : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0522 MAI/0522 Mémoire Bibliothéque des sciences Anglais Disponible
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