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Titlebook: Knowledge Science, Engineering and Management; 12th International C Christos Douligeris,Dimitris Karagiannis,Dimitris Conference proceedin

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樓主: GERM
11#
發(fā)表于 2025-3-23 10:30:26 | 只看該作者
Centralized Reasoning Translation and Its Computing Complexity for Heterogeneous Semantic Mappings. The current research focuses on the homogeneous bridge rules that only contain atomic elements. In this paper, the research is extended to the D3L reasoning problem with heterogeneous bridge rules that contain composite elements in subset ends. The regularity of the distributed knowledge base is d
12#
發(fā)表于 2025-3-23 16:08:42 | 只看該作者
13#
發(fā)表于 2025-3-23 20:09:42 | 只看該作者
14#
發(fā)表于 2025-3-23 23:03:07 | 只看該作者
15#
發(fā)表于 2025-3-24 05:51:03 | 只看該作者
16#
發(fā)表于 2025-3-24 09:07:20 | 只看該作者
17#
發(fā)表于 2025-3-24 12:35:28 | 只看該作者
18#
發(fā)表于 2025-3-24 18:09:27 | 只看該作者
19#
發(fā)表于 2025-3-24 20:16:07 | 只看該作者
A Network Embedding and Clustering Algorithm for Expert Recommendation Serviceeatures of various tasks based on graphs, including classification, clustering, link prediction and visualization. Currently, network embedding algorithms have evolved from considering structures only to considering structures and contents both. However, how to effectively integrate the high-order p
20#
發(fā)表于 2025-3-25 00:14:45 | 只看該作者
Mixing-RNN: A Recommendation Algorithm Based on Recurrent Neural Networktering algorithms face challenges to provide accurate recommendation when users’ interest and context suddenly changed. In this paper, we present a new Recurrent Neural Network-based model, namely Mixing-RNN that is able to capture time and context changes for item recommendation. In particular, Mix
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