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

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

書(shū)目名稱(chēng)Web Information Systems and Applications網(wǎng)絡(luò)公開(kāi)度

書(shū)目名稱(chēng)Web Information Systems and Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名

書(shū)目名稱(chēng)Web Information Systems and Applications被引頻次

書(shū)目名稱(chēng)Web Information Systems and Applications被引頻次學(xué)科排名

書(shū)目名稱(chēng)Web Information Systems and Applications年度引用

書(shū)目名稱(chēng)Web Information Systems and Applications年度引用學(xué)科排名

書(shū)目名稱(chēng)Web Information Systems and Applications讀者反饋

書(shū)目名稱(chēng)Web Information Systems and Applications讀者反饋學(xué)科排名

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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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只看該作者 · 發(fā)表于 2025-3-23 05:38:04

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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