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Advancing recommender systems with g...

Liu, Fan.

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Advancing recommender systems with graph convolutional networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advancing recommender systems with graph convolutional networks/ by Fan Liu, Liqiang Nie.
Author:	Liu, Fan.
other author:	Nie, Liqiang.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xv, 157 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Recommender systems (Information filtering) -
Online resource:	https://doi.org/10.1007/978-3-031-85093-6
ISBN:	9783031850936

Advancing recommender systems with graph convolutional networks
Liu, Fan.

Advancing recommender systems with graph convolutional networks[electronic resource] /by Fan Liu, Liqiang Nie. - Cham :Springer Nature Switzerland :2025. - xv, 157 p. :ill., digital ;24 cm.

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.

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 creation of enriched and expressive representations. The book focuses on two overarching problem categories: the first area deals with problems specific to GCN-based recommendation models, including over-smoothing, noisy neighboring nodes, and interpretability limitations. The second one encompasses broader challenges in recommendation systems that GCN-based methods are particularly well-suited to address as the attribute missing problem or feature misalignment. Through rigorous exploration of these challenges, this book presents innovative GCN-based solutions to push the boundaries of recommender system design. To this end, techniques such as interest-aware message-passing strategy, cluster-based collaborative filtering, semantic aspects extraction, attribute-aware attention mechanisms, and light graph transformer are presented. Each chapter combines theoretical insights with practical implementations and experimental validation, offering a comprehensive resource for researchers, advanced professionals, and graduate students alike.

ISBN: 9783031850936

Standard No.: 10.1007/978-3-031-85093-6doiSubjects--Topical Terms:

1002434
Recommender systems (Information filtering)

LC Class. No.: ZA3084

Dewey Class. No.: 005.56

Advancing recommender systems with graph convolutional networks
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505 0 $a 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.
520 $a 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 creation of enriched and expressive representations. The book focuses on two overarching problem categories: the first area deals with problems specific to GCN-based recommendation models, including over-smoothing, noisy neighboring nodes, and interpretability limitations. The second one encompasses broader challenges in recommendation systems that GCN-based methods are particularly well-suited to address as the attribute missing problem or feature misalignment. Through rigorous exploration of these challenges, this book presents innovative GCN-based solutions to push the boundaries of recommender system design. To this end, techniques such as interest-aware message-passing strategy, cluster-based collaborative filtering, semantic aspects extraction, attribute-aware attention mechanisms, and light graph transformer are presented. Each chapter combines theoretical insights with practical implementations and experimental validation, offering a comprehensive resource for researchers, advanced professionals, and graduate students alike.
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650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
700 1 $a Nie, Liqiang. $3 3781274
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