Tun Lu – författare
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This book constitutes the refereed proceedings of the 13th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2018, held in Guilin, China, in August 2018.
The 33 revised full papers presented along with the 13 short papers were carefully reviewed and selected from 150 submissions. The papers of this volume are organized in topical sections on: collaborative models, approaches, algorithms, and systems, social computing, data analysis and machine learning for CSCW and social computing.
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The 52 revised full papers and 10 short papers were carefully reviewed and selected from 169 submissions. The papers of this volume are organized in topical sections on: collaborative models, approaches, algorithms, and systems; social computing (online communities, crowdsourcing, recommendation, sentiment analysis, etc.); AI for CSCW and social computing.
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Kommande
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This two part LNCS volumes constitutes the refereed proceedings of the 19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024, held in Hohhot, China, in July 12-14, 2024.
The 52 full papers and 18 short papers included in this book were carefully reviewed and selected from 200 submissions. They were organized in topical sections as follows :
Part-I : Trend; CSCW and Social Computing, and Human-Machine-Things Fusion and Human-AI Collaborative Computing.
Part-II : Crowd Cooperative Computing and Evolutionary Computation, and Collaborative Data, Software and Services.
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This two part LNCS volumes constitutes the refereed proceedings of the 19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024, held in Hohhot, China, in July 12- 14, 2024.
The 52 full papers and 18 short papers included in this book were carefully reviewed and selected from 200 submissions. They were organized in topical sections as follows :
Part-I : Trend; CSCW and Social Computing, and Human-Machine-Things Fusion and Human-AI Collaborative Computing.
Part-II : Crowd Cooperative Computing and Evolutionary Computation, and Collaborative Data, Software and Services.
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This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft ''s open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.
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