Contact
How to shop
Delivery
Shopping guide
Advancing Recommender Systems with Graph Convolution Networks
English
325 b
Up to 30 days for returns
Customers also purchased
/
Adobe ePub DRM
2 879
Ft
/
Hardback
15 397
Ft
/
Paperback
8 855
Ft
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.
About the book
English
Categories
Give this book today
It's easy
1 Add to cart and choose Deliver as present at the checkout 2 We'll send you a voucher 3 The book will arrive at the recipient's addressYou might also be interested in
/
Paperback
5 685
Ft
/
Paperback
8 431
Ft
/
Paperback
4 441
Ft
/
Paperback
7 099
Ft
/
Paperback
3 243
Ft
/
Paperback
16 378
Ft
Hi! I'm Libroamiko, your book advisor.
How can I help you?