LEADER 03590nam 22005895 450 001 9910992791503321 005 20250330141131.0 010 $a9783031850936 010 $a3031850939 024 7 $a10.1007/978-3-031-85093-6 035 $a(CKB)38166502000041 035 $a(MiAaPQ)EBC31981026 035 $a(Au-PeEL)EBL31981026 035 $a(DE-He213)978-3-031-85093-6 035 $a(OCoLC)1513254041 035 $a(EXLCZ)9938166502000041 100 $a20250330d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvancing Recommender Systems with Graph Convolutional Networks /$fby Fan Liu, Liqiang Nie 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (166 pages) 311 08$a9783031850929 311 08$a3031850920 327 $aPreface -- 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. 330 $aThis 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. 606 $aInformation storage and retrieval systems 606 $aArtificial intelligence 606 $aNeural networks (Computer science) 606 $aInformation Storage and Retrieval 606 $aArtificial Intelligence 606 $aMathematical Models of Cognitive Processes and Neural Networks 615 0$aInformation storage and retrieval systems. 615 0$aArtificial intelligence. 615 0$aNeural networks (Computer science). 615 14$aInformation Storage and Retrieval. 615 24$aArtificial Intelligence. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 676 $a025.04 700 $aLiu$b Fan$01783717 701 $aNie$b Liqiang$01783719 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910992791503321 996 $aAdvancing Recommender Systems with Graph Convolutional Networks$94349107 997 $aUNINA