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Titlebook: Database Systems for Advanced Applications; 26th International C Christian S. Jensen,Ee-Peng Lim,Chih-Ya Shen Conference proceedings 2021 T

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發(fā)表于 2025-3-21 18:26:27 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Database Systems for Advanced Applications
副標題26th International C
編輯Christian S. Jensen,Ee-Peng Lim,Chih-Ya Shen
視頻videohttp://file.papertrans.cn/264/263420/263420.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Database Systems for Advanced Applications; 26th International C Christian S. Jensen,Ee-Peng Lim,Chih-Ya Shen Conference proceedings 2021 T
描述.The three-volume set LNCS 12681-12683 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021, held in Taipei, Taiwan, in April 2021...The total of 156 papers presented in this three-volume set was carefully reviewed and selected from 490 submissions...The topic areas for the selected papers include information retrieval, search and recommendation techniques; RDF, knowledge graphs, semantic web, and knowledge management; and spatial, temporal, sequence, and streaming data management, while the dominant keywords are network, recommendation, graph, learning, and model. These topic areas and keywords shed the light on the direction where the research in DASFAA is moving towards...Due to the Corona pandemic this event was held virtually..
出版日期Conference proceedings 2021
關(guān)鍵詞big data; graph data management; spatial data management; temporal data management; data mining; machine
版次1
doihttps://doi.org/10.1007/978-3-030-73200-4
isbn_softcover978-3-030-73199-1
isbn_ebook978-3-030-73200-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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https://doi.org/10.1007/978-1-4757-4854-3nes BPR loss with adaptive margin and similarity loss for the similarities learning. Extensive experiments on three benchmarks show that our model is consistently better than the latest state-of-the-art models.
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Mau Mau Inventions and Reinventionsention module, temporal textual information is brought in as a clue to dynamically select friends that are helpful for contextual purchase intentions as an implicit combination. We validate our proposed method on two large subsets of real-world local business dataset Yelp, and our method outperforms
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Mau Mau Inventions and Reinventionsmize their margin. This intense adversarial competition provides increasing learning difficulties and constantly pushes the boundaries of its performance. Extensive experiments on three real-world datasets demonstrate the superiority of our methods over some strong baselines and prove the effectiven
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https://doi.org/10.1057/978-1-137-53913-7ve subsequence selection strategy to update model. Moreover, to improve the effectiveness and efficiency of online model training, we propose a novel negative sampling strategy based on GAN to generate the most informative negative samples and use Gumble-Softmax to overcome the gradient block proble
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The State and New Religious Movementsr, referred to as recurrent tree transformer, and utilize this new transformer to generate a unified user interest representation. The last fully connected layer is utilized to model the interaction between this unified representation and item embedding. Comprehensive experiments are conducted on tw
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