Advancing Recommender Systems with GCNs by Fan Liu (.PDF)+

Posted on by

File Size: 10 MB

Advancing Recommender Systems with Graph Convolutional Networks by Fan Liu, Liqiang Nie
Requirements: .ePUB, .PDF reader, 10 MB
Overview: 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.
Genre: Non-Fiction > Tech & Devices

Free Download links:

https://trbt.cc/nlao7wrny9j4.html

https://katfile.com/zmag73qkz2yt/Advancing_Recommender_Systems_with_GCNs.rar.html