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Titlebook: Advances in Knowledge Discovery and Data Mining; 24th Pacific-Asia Co Hady W. Lauw,Raymond Chi-Wing Wong,Sinno Jialin Pa Conference proceed

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樓主: 解毒藥
21#
發(fā)表于 2025-3-25 05:03:03 | 只看該作者
22#
發(fā)表于 2025-3-25 09:37:43 | 只看該作者
Conference proceedings 2020d Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020.?The conference was held virtually due to the COVID-19 pandemic...The 135 full papers presented were carefully reviewed and selected from 628 submissions. The papers present new ideas, original research results, and practi
23#
發(fā)表于 2025-3-25 12:39:48 | 只看該作者
Elspeth Cameron Ritchie,Maria D. Llorenteor real-time performance tracking. We describe the extended functionalities of the adapted prequential evaluation protocol, and develop a competitive recommendation algorithm on top of .’s implementation of a Hoeffding Tree. We compare our algorithm to other known baselines for the next-item prediction task across three different domains.
24#
發(fā)表于 2025-3-25 18:51:41 | 只看該作者
0302-9743 scovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020.?The conference was held virtually due to the COVID-19 pandemic...The 135 full papers presented were carefully reviewed and selected from 628 submissions. The papers present new ideas, original research results,
25#
發(fā)表于 2025-3-25 20:14:47 | 只看該作者
26#
發(fā)表于 2025-3-26 00:33:12 | 只看該作者
27#
發(fā)表于 2025-3-26 04:20:39 | 只看該作者
Medical Care for Homeless Persons alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model.
28#
發(fā)表于 2025-3-26 10:33:01 | 只看該作者
Matthew Brown MD,Kevin C. Chung MD, MSnces and the system’s fairness status are jointly compressed into the state representation to generate recommendations. FairRec aims at maximizing our designed cumulative reward that combines accuracy and fairness. Extensive experiments validate that FairRec can improve fairness, while preserving good recommendation quality.
29#
發(fā)表于 2025-3-26 14:52:48 | 只看該作者
30#
發(fā)表于 2025-3-26 20:36:31 | 只看該作者
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