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Titlebook: Web Information Systems and Applications; 17th International C Guojun Wang,Xuemin Lin,Genggeng Liu Conference proceedings 2020 Springer Nat

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書目名稱Web Information Systems and Applications
副標(biāo)題17th International C
編輯Guojun Wang,Xuemin Lin,Genggeng Liu
視頻videohttp://file.papertrans.cn/1022/1021546/1021546.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Web Information Systems and Applications; 17th International C Guojun Wang,Xuemin Lin,Genggeng Liu Conference proceedings 2020 Springer Nat
描述.This book constitutes the proceedings of the 17th International Conference on Web Information Systems and Applications, WISA 2020, held in Guangzhou, China, in September 2020. The 42 full papers and 16 short papers presented were carefully reviewed and selected from 165 submissions. The papers are grouped in topical sections on world wide web, recommendation, query processing and algorithm, natural language processing, machine learning, graph query, edge computing and data mining, data privacy and security, and blockchain..
出版日期Conference proceedings 2020
關(guān)鍵詞artificial intelligence; computer networks; computer security; computer systems; data mining; data securi
版次1
doihttps://doi.org/10.1007/978-3-030-60029-7
isbn_softcover978-3-030-60028-0
isbn_ebook978-3-030-60029-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

書目名稱Web Information Systems and Applications影響因子(影響力)




書目名稱Web Information Systems and Applications影響因子(影響力)學(xué)科排名




書目名稱Web Information Systems and Applications網(wǎng)絡(luò)公開度




書目名稱Web Information Systems and Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Web Information Systems and Applications被引頻次




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Web Information Systems and Applications978-3-030-60029-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/w/image/1021546.jpg
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https://doi.org/10.1007/978-3-030-60029-7artificial intelligence; computer networks; computer security; computer systems; data mining; data securi
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Effective Knowledge-Aware Recommendation via Graph Convolutional Networkss paper, we propose the improved Knowledge-aware Graph Neural Networks with Label Smoothness Regularization (iKGNN-LS) model, which makes two improvements to KGNN-LS: (1) In iKGNN-LS, by introducing user-specific entity scoring functions, the edge weights are determined jointly by personalized user
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Micro-nano Depth Information Recovery Method Based on TV Regularizationrization parameters, the depth of the recovery is avoided. The information is too smooth and retains more detail. The depth information recovery experiments of the standard 500?nm scale grid show that compared with the Tikhonov regularization method and the TSVD regularization method, the L_TV algor
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