Titlebook: Big Data and Social Computing; 8th China National C Xiaofeng Meng,Yang Chen,Zi-Ke Zhang Conference proceedings 2023 The Editor(s) (if appli

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1865-0929 China, from?July 15–17, 2023..The 23 full papers and 3 short papers presented in this volume were carefully reviewed and selected from a total of 141 submissions. The papers in the volume are organized according to the following topical headings:?Digital Technology and Sustainable Development;?Soci

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Dominique B?ttcher,Uwe T. Bornscheuerention-enhanced mechanism to explore the implicit interactions between mobile users and locations themselves. Conducting adequate comparative experiments on two real-world telecom datasets has demonstrated that our model achieves superior performance. Additionally, our model has been proven effective in addressing data sparsity issues.

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強(qiáng)制性

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Location Recommendations Based on?Multi-view Learning and?Attention-Enhanced Graph Networksention-enhanced mechanism to explore the implicit interactions between mobile users and locations themselves. Conducting adequate comparative experiments on two real-world telecom datasets has demonstrated that our model achieves superior performance. Additionally, our model has been proven effective in addressing data sparsity issues.

稱(chēng)贊

Driving Style Classification and?Dynamic Recognition Considering Traffic State-means clustering method. The results of variance test showed that 11 parameters are significantly different between groups. This shows that the driving style classification method proposed in this paper can distinguish different driving styles.

Compatriot

Who Connects Wikipedia? A Deep Analysis of Node Roles and Connection Patterns in Wikilink Network and are connected through a plurality of club nodes across communities, which play an integral role in the transfer and flow of information throughout the network. This study reveals the roles and information transfer mechanisms of key nodes in the network and provides a reference for building more efficient knowledge networks.

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A Root Cause Localization Method of Base Station Cells with Poor Quality Using AI?+?SHAPaper innovatively proposes a method that uses AI?+?SHAP model interpretation to locate the root cause of base station cells with poor quality, which greatly improves the efficiency and accuracy of positioning, and the feasibility and effectiveness of the model are verified through extensive experiments.

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