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Titlebook: Database Systems for Advanced Applications; 25th International C Yunmook Nah,Bin Cui,Steven Euijong Whang Conference proceedings 2020 Sprin

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書(shū)目名稱Database Systems for Advanced Applications
副標(biāo)題25th International C
編輯Yunmook Nah,Bin Cui,Steven Euijong Whang
視頻videohttp://file.papertrans.cn/264/263435/263435.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Database Systems for Advanced Applications; 25th International C Yunmook Nah,Bin Cui,Steven Euijong Whang Conference proceedings 2020 Sprin
描述.The 4 volume set LNCS 12112-12114 constitutes the papers of the 25th International Conference on Database Systems for Advanced Applications which will be held online in September 2020...The 119 full papers presented together with 19 short papers plus 15 demo papers and 4 industrial papers in this volume were carefully reviewed and selected from a total of 487 submissions...The conference program presents the state-of-the-art R&D activities in database systems and their applications. It provides a forum for technical presentations and discussions among database researchers, developers and users from academia, business and industry..
出版日期Conference proceedings 2020
關(guān)鍵詞artificial intelligence; communication systems; computer networks; computer science; computer systems; da
版次1
doihttps://doi.org/10.1007/978-3-030-59416-9
isbn_softcover978-3-030-59415-2
isbn_ebook978-3-030-59416-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Contemporary Issues in Marketingl reviews for rating prediction. Extensive experiments on four real-world datasets demonstrate that our model achieves the state-of-the-art performance in both rating prediction and review generation tasks.
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https://doi.org/10.1057/9781137025807quantify result error progressively. We implemented this method in our system called Parrot on top of Apache Spark and used real-world data to test its performance. Experiments demonstrate that our method is 2.4x–19.7x faster to get a result within 1% error while the confidence interval always covers the accurate results very well.
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Adversarial Generation of Target Review for Rating Predictionl reviews for rating prediction. Extensive experiments on four real-world datasets demonstrate that our model achieves the state-of-the-art performance in both rating prediction and review generation tasks.
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0302-9743 which will be held online in September 2020...The 119 full papers presented together with 19 short papers plus 15 demo papers and 4 industrial papers in this volume were carefully reviewed and selected from a total of 487 submissions...The conference program presents the state-of-the-art R&D activi
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Conference proceedings 2020olume were carefully reviewed and selected from a total of 487 submissions...The conference program presents the state-of-the-art R&D activities in database systems and their applications. It provides a forum for technical presentations and discussions among database researchers, developers and users from academia, business and industry..
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