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[null,"1.","Record Nr.",null,"UNINA9910992791503321"]
[null,null,"Autore",null,"Liu Fan"]
[null,null,"Titolo",null,"Advancing Recommender Systems with Graph Convolutional Networks / / by Fan Liu, Liqiang Nie"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"9783031850936"]
[{"$nulls":4},"3031850939"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (166 pages)"]
[null,null,"Disciplina",null,"025.04"]
[null,null,"Soggetti",null,"Information storage and retrieval systems"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Neural networks (Computer science)"]
[{"$nulls":4},"Information Storage and Retrieval"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Mathematical Models of Cognitive Processes and Neural Networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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."]
[null,null,null,"Sommario/riassunto","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-"]
[{"$nulls":4},"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."]