Titlebook: Database Systems for Advanced Applications; 23rd International C Jian Pei,Yannis Manolopoulos,Jianxin Li Conference proceedings 2018 Spring

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書(shū)目名稱(chēng)Database Systems for Advanced Applications影響因子(影響力)




書(shū)目名稱(chēng)Database Systems for Advanced Applications影響因子(影響力)學(xué)科排名




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書(shū)目名稱(chēng)Database Systems for Advanced Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Database Systems for Advanced Applications被引頻次




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書(shū)目名稱(chēng)Database Systems for Advanced Applications年度引用




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書(shū)目名稱(chēng)Database Systems for Advanced Applications讀者反饋




書(shū)目名稱(chēng)Database Systems for Advanced Applications讀者反饋學(xué)科排名

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https://doi.org/10.1007/978-3-319-91452-7artificial intelligence; computational linguistics; computer networks; data mining; data security; image

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978-3-319-91451-0Springer International Publishing AG, part of Springer Nature 2018

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Isosmotic Fluid Transport across Epithelia,from different perspectives, on one hand, most of the existing methods only focus on leveraging the plain network structure, ignoring the abundant attribute information of nodes. On the other hand, for some methods integrating the attribute information, only the lower-order proximities (e.g. microsc

semble

semble

Moises Gaviria,Glorianna Montasng network embedding methods mostly focus on unsigned networks and ignore the negative interactions between users. In this paper, we investigate the problem of learning representations for signed networks and present a novel deep network structure to incorporate both the balance and status theory in

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Female Circumcision/Mutilation/Cutting. As a result, POI recommendation systems, which play a vital role in connecting users and POIs, have received extensive attention from both research and industry communities in the past few years. The challenges of POI recommendation come from the very sparse user check-in records with only positiv

手段

Olusegun Badejoko,Uchenna Onwudiegwuusers or items are elicited, more effective the recommendations are. However, almost all user rating datasets are sparse in the real-world applications. To acquire more ratings, the active learning based methods have been used to selectively choose the items (called interview items) to ask users for