Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems. GNNs have proven to be effective in addressing the challenges po...
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Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems. GNNs have proven to be effective in addressing the challenges posed by recommendation systems by efficiently modeling graphs in which nodes represent users or items and edges denote preference relationships. However, current GNN techniques represent nodes by means of a single static vector, which may inadequately capture the intricate complexities of users and items. To overcome these limitations, we propose a solution integrating a cutting-edge model inspired by category theory: Sheaf4Rec. Unlike single vector representations, Sheaf Neural Networks and their corresponding Laplacians represent each node (and edge) using a vector space. Our approach takes advantage of this theory and results in a more comprehensive representation that can be effectively exploited during inference, providing a versatile method applicable to a wide range of graph-related tasks and demonstrating unparalleled performance. Our proposed model exhibits a noteworthy relative improvement of up to 8.53% on F1-Score@10 and an impressive increase of up to 11.29% on NDCG@10, outperforming existing state-of-the-art models such as Neural Graph Collaborative Filtering (NGCF), KGTORe and other recently developed GNN-based models. In addition to its superior predictive capabilities, Sheaf4Rec shows remarkable improvements in terms of efficiency: we observe substantial runtime improvements ranging from 2.5% up to 37% when compared to other GNN-based competitor models, indicating a more efficient way of handling information while achieving better performance. Code is available at https://***/antoniopurificato/Sheaf4Rec.
This book gathers high-quality papers presented at the 5th International Conference on Intelligent Computing, Communication & Devices (ICCD 2019), held in Xi'an, China on November 22–24, 2019. The contributio...
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ISBN:
(数字)9789811558870
ISBN:
(纸本)9789811558863
This book gathers high-quality papers presented at the 5th International Conference on Intelligent Computing, Communication & Devices (ICCD 2019), held in Xi'an, China on November 22–24, 2019. The contributions focus on emergent fields of intelligent computing and the development of a new generation of intelligent systems. Further, they discuss virtually all dimensions of the intelligent sciences, including intelligent computing, intelligent communication and intelligent devices.
This book constitutes the thoroughly refereed proceedings of the 9th Italian Research Conference on Digital Libraries, held in Rome, Italy, in January/February 2013. The 18 full papers presented together with an invit...
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ISBN:
(数字)9783642543470
ISBN:
(纸本)9783642543463
This book constitutes the thoroughly refereed proceedings of the 9th Italian Research Conference on Digital Libraries, held in Rome, Italy, in January/February 2013. The 18 full papers presented together with an invited paper and a panel paper were selected from extended versions of the presentations given at the conference. The papers then went through an additional round of reviewing and revision after the event. The papers are organized in topical sections on information access; Digital Library (DL) architecture; DL projects; semantics and DLs; models and evaluation for DLs; DL applications; discussing DL perspectives.
Conferences play a major role in some disciplines such as computer science and are often used in research quality evaluation exercises. Differently from journals and books, for which ISSN and ISBN codes provide unambi...
Internal models are nowadays customarily used in different domains of science and engineering to describe how living organisms or artificial computational units embed their acquired knowledge about recurring events ta...
Internal models are nowadays customarily used in different domains of science and engineering to describe how living organisms or artificial computational units embed their acquired knowledge about recurring events taking place in the surrounding environment. This article reviews the internal model principle in control theory, bioengineering, and neuroscience, illustrating the fundamental concepts and theoretical developments of the few last decades of research.
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