商品描述
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.
商品描述(中文翻譯)
本書系統性地探討了與圖卷積網絡（GCNs）在推薦系統應用相關的可擴展性和有效性挑戰。透過有效建模圖結構，GCNs 在捕捉用戶與項目之間的高階關係方面表現出色，使得能夠創建豐富且具表現力的表示。
本書專注於兩個主要問題類別：第一個領域處理與基於 GCN 的推薦模型特定的問題，包括過度平滑、噪聲鄰近節點和可解釋性限制。第二個領域則涵蓋了推薦系統中更廣泛的挑戰，這些挑戰是基於 GCN 的方法特別適合解決的，例如屬性缺失問題或特徵不對齊。通過對這些挑戰的嚴謹探索，本書提出了創新的基於 GCN 的解決方案，以推動推薦系統設計的邊界。為此，介紹了興趣感知的消息傳遞策略、基於聚類的協同過濾、語義方面提取、屬性感知的注意機制以及輕量級圖變壓器等技術。
每一章結合了理論見解與實際實現和實驗驗證，為研究人員、高級專業人士和研究生提供了全面的資源。
作者簡介
Fan Liu is a Research Fellow with the School of Computing, National University of Singapore (NUS). His research interests lie primarily in multimedia computing and information retrieval. His work has been published in a set of top forums, including ACM SIGIR, MM, WWW, TKDE, TOIS, TMM, and TCSVT. He is an area chair of ACM MM and a senior PC member of CIKM. Liqiang Nie is Professor at and Dean of the School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen). His research interests are primarily in multimedia computing and information retrieval. He has co-authored more than 200 articles and four books. He is a regular area chair of ACM MM, NeurIPS, IJCAI, and AAAI, and a member of ICME steering committee. He has received many awards, like the ACM MM and SIGIR best paper honorable mention in 2019, SIGMM rising star in 2020, TR35 China 2020, DAMO Academy Young Fellow in 2020, and SIGIR best student paper in 2021.
作者簡介(中文翻譯)
劉凡是新加坡國立大學（NUS）計算機學院的研究員。他的研究興趣主要集中在多媒體計算和資訊檢索。他的研究成果已發表於多個頂尖論壇，包括ACM SIGIR、MM、WWW、TKDE、TOIS、TMM和TCSVT。他是ACM MM的區域主席以及CIKM的資深程序委員會成員。 聶立強是哈爾濱工業大學（深圳）計算機科學與技術學院的教授及院長。他的研究興趣主要在於多媒體計算和資訊檢索。他共同撰寫了超過200篇文章和四本書籍。他是ACM MM、NeurIPS、IJCAI和AAAI的常任區域主席，並且是ICME指導委員會的成員。他獲得了許多獎項，如2019年ACM MM和SIGIR最佳論文榮譽提名、2020年SIGMM新星、2020年TR35中國、2020年DAMO Academy青年研究員，以及2021年SIGIR最佳學生論文。
