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Advancing Recommender Systems with Graph Convolutional Networks

This book systematically examines scalability and effectiveness challenges related to the application of graph convolutional networks (GCNs) in recommender systems. By effectively modeling graph structures, GCNs excel in capturing high-order relationships between users and items, enabling the creati...

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Autors principals: Liu, Fan (Autor), Nie, Liqiang (Autor)
Autor corporatiu: SpringerLink (Online service)
Format: Electrònic eBook
Idioma:anglès
Publicat: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edició:1st ed. 2025.
Matèries:
Information storage and retrieval systems.
Artificial intelligence.
Neural networks (Computer science) .
Information Storage and Retrieval.
Artificial Intelligence.
Mathematical Models of Cognitive Processes and Neural Networks.
Accés en línia:https://doi.org/10.1007/978-3-031-85093-6
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Taula de continguts:
  • Preface
  • 1) Introduction
  • 2) Interest-aware Message-Passing Graph Convolutional Network
  • 3) Cluster-based Graph Collaborative Filtering
  • 4) Semantic Aspect-aware Graph Convolutional Network
  • 5) Attribute-aware Attentive Graph Convolutional Network
  • 6) Light Graph Transformer Model
  • 7) Research Frontiers.

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