03590nam 22005895 450 991099279150332120250330141131.09783031850936303185093910.1007/978-3-031-85093-6(CKB)38166502000041(MiAaPQ)EBC31981026(Au-PeEL)EBL31981026(DE-He213)978-3-031-85093-6(OCoLC)1513254041(EXLCZ)993816650200004120250330d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAdvancing Recommender Systems with Graph Convolutional Networks /by Fan Liu, Liqiang Nie1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (166 pages)9783031850929 3031850920 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.Information storage and retrieval systemsArtificial intelligenceNeural networks (Computer science)Information Storage and RetrievalArtificial IntelligenceMathematical Models of Cognitive Processes and Neural NetworksInformation storage and retrieval systems.Artificial intelligence.Neural networks (Computer science).Information Storage and Retrieval.Artificial Intelligence.Mathematical Models of Cognitive Processes and Neural Networks.025.04Liu Fan1783717Nie Liqiang1783719MiAaPQMiAaPQMiAaPQBOOK9910992791503321Advancing Recommender Systems with Graph Convolutional Networks4349107UNINA