Advancing Recommender Systems with Graph Convolutional Networks
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Sprache:Englisch
Fr. 162.00
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Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
30.03.2025
Abbildungen
XV, 157 p.
Verlag
SpringerSeitenzahl
157
Maße (L/B/H)
23.5/15.5/1 cm
Gewicht
277 g
Sprache
Englisch
ISBN
978-3-031-85092-9
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.
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