+- HHWForum.hu (https://hhwforum.hu)
+-- Fórum: Letöltések (https://hhwforum.hu/forumdisplay.php?fid=9)
+--- Fórum: E-könyvek (https://hhwforum.hu/forumdisplay.php?fid=57)
+---- Fórum: Külföldi könyvek (https://hhwforum.hu/forumdisplay.php?fid=64)
+---- Téma: Advancing Recommender Systems with Graph Convolutional Networks (/showthread.php?tid=273826)
RE: Advancing Recommender Systems with Graph Convolutional Networks - book24h - 2025-04-05
![[Kép: 0d2abff79ef1e5287ca7d9bca6bac64a.webp]](https://i124.fastpic.org/big/2025/0405/4a/0d2abff79ef1e5287ca7d9bca6bac64a.webp)
Free Download Advancing Recommender Systems with Graph Convolutional Networks
by Fan Liu and Liqiang Nie
English | 2025 | ISBN: 3031850920 | 166 Pages | True PDF | 4.78 MB
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.
Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me
Idézet:A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.Links are Interchangeable - Single Extraction