Advancing Recommender Systems with Graph Convolutional Networks - Softcover

Liu, Fan; Nie, Liqiang

Softcover

ISBN 10: 3031850920 ISBN 13: 9783031850929

Verlag: Springer, 2025

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den Autor

Fan Liu is a Research Fellow with the School of Computing, National University of Singapore (NUS). His research interests lie primarily in multimedia computing and information retrieval. His work has been published in a set of top forums, including ACM SIGIR, MM, WWW, TKDE, TOIS, TMM, and TCSVT. He is an area chair of ACM MM and a senior PC member of CIKM.

Liqiang Nie is Professor at and Dean of the School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen). His research interests are primarily in multimedia computing and information retrieval. He has co-authored more than 200 articles and four books. He is a regular area chair of ACM MM, NeurIPS, IJCAI, and AAAI, and a member of ICME steering committee. He has received many awards, like the ACM MM and SIGIR best paper honorable mention in 2019, SIGMM rising star in 2020, TR35 China 2020, DAMO Academy Young Fellow in 2020, and SIGIR best student paper in 2021.

Von der hinteren Coverseite

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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2025
• Sprache: Englisch
• ISBN 10: 3031850920
• ISBN 13: 9783031850929
• Einband: Taschenbuch
• Anzahl der Seiten: 176
• Kontakt zum Hersteller: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Deutschland