Torrent details for "Fan L. Advancing Recommender Systems with Graph Convolutional Ne…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
4.8 MB
Info Hash:
6723D5ED15FF52E6592D19EDCEC9FA46AE266D5A
Added By:
Added:
April 20, 2026, 6:18 p.m.
Stats:
|
(Last updated: April 20, 2026, 6:24 p.m.)
| File | Size |
|---|---|
| Fan L. Advancing Recommender Systems with Graph Convolutional Networks 2025.pdf | 4.8 MB |
Name
DL
Uploader
Size
S/L
Added
NOTE
SOURCE: Fan L. Advancing Recommender Systems with Graph Convolutional Networks 2025
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format 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
×