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books
| book details |
Advancing Recommender Systems with Graph Convolutional Networks
By (author) Fan Liu, By (author) Liqiang Nie
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| on special |
normal price: R 4 173.95
Price: R 3 965.95
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| book description |
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.
| product details |
Normally shipped |
Publisher | Springer International Publishing AG
Published date | 30 Mar 2025
Language |
Format | Paperback / softback
Pages | 157
Dimensions | 235 x 155 x 0mm (L x W x H)
Weight | 0g
ISBN | 978-3-0318-5092-9
Readership Age |
BISAC | computers / information storage & retrieval
| other options |
Normally shipped |
Readership Age |
Normal Price | R 4 998.95
Price | R 4 748.95
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Mason Coile
Paperback / softback
224 pages
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