國立高雄大學圖資館 |

登入

回首頁

Advancing recommender systems with graph convolutional networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advancing recommender systems with graph convolutional networksby Fan Liu, Liqiang Nie.
作者:	Liu, Fan.
其他作者:	Nie, Liqiang.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xv, 157 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Recommender systems (Information filtering)
電子資源:	https://doi.org/10.1007/978-3-031-85093-6
ISBN:	9783031850936$q(electronic bk.)

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$q(electronic bk.)

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

310886
Recommender systems (Information filtering)

LC Class. No.: ZA3084

Dewey Class. No.: 005.56

Advancing recommender systems with graph convolutional networks
LDR:02691nmm a2200349 a 4500 001 679633
003 DE-He213
005 20250330141131.0
006 m d
007 cr nn 008maaau
008 251110s2025 sz s 0 eng d
020 $a 9783031850936$q(electronic bk.)
020 $a 9783031850929$q(paper)
024 7 $a 10.1007/978-3-031-85093-6 $2 doi
035 $a 978-3-031-85093-6
040 $a GP $c GP
041 0 $a eng
050 4 $a ZA3084
072 7 $a UNH $2 bicssc
072 7 $a UND $2 bicssc
072 7 $a COM030000 $2 bisacsh
072 7 $a UNH $2 thema
072 7 $a UND $2 thema
082 0 4 $a 005.56 $2 23
090 $a ZA3084 $b .L783 2025
100 1 $a Liu, Fan. $3 958404
245 1 0 $a Advancing recommender systems with graph convolutional networks $h [electronic resource] / $c by Fan Liu, Liqiang Nie.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xv, 157 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Recommender systems (Information filtering) $3 310886
650 1 4 $a Information Storage and Retrieval. $3 274190
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
700 1 $a Nie, Liqiang. $3 854463
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-85093-6
950 $a Computer Science (SpringerNature-11645)