昆虫
发表于 2025-3-28 17:36:24
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施魔法
发表于 2025-3-28 22:47:33
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显而易见
发表于 2025-3-29 01:28:23
Hypergraph Enhanced Contrastive Learning for News Recommendation a fine-grained topic level and encodes useful information into the representations of users and items. Meanwhile, the designed hypergraph structure learning module enhances the discrimination ability and enriches the complex high-order dependencies, which improves the presentation quality of the re
tackle
发表于 2025-3-29 03:58:21
A Session Recommendation Model Based on Heterogeneous Graph Neural Networkeature representations, and the attention mechanism weights the features according to each user’s current interests. We evaluate our method on two public datasets, and our results show that our model outperforms existing approaches in terms of accuracy and robustness. In conclusion, our proposed met
甜得发腻
发表于 2025-3-29 07:21:35
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刺穿
发表于 2025-3-29 14:28:42
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cloture
发表于 2025-3-29 16:24:03
CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-Start Problem of Recommendags of old items as its base embedding; S-EG generates its shift embedding not only with its attribute features but also with the average ID embedding of the users who interacted with it. The final meta embedding is obtained by adding up the base embedding and the shift embedding. We conduct extensiv
Malleable
发表于 2025-3-29 20:47:31
A Graph Neural Network for Cross-domain Recommendation Based on Transfer and Inter-domain Contrastive transfer. Finally, contrastive learning is performed on the overlapping users or items in the two domains, and the self-supervised contrastive learning task and supervised learning task are jointly trained to alleviate the differences between the two domain.
FILTH
发表于 2025-3-30 03:18:47
Moming Tang,Chengyu Wang,Jianing Wang,Cen Chen,Ming Gao,Weining Qianort verschiedene Projekte im Bereich der Gesundheitsökonomie und Versorgungsforschung. Die Arbeit entstand als Dissertation an der Universität Greifswald, an de978-3-658-32542-8978-3-658-32543-5Series ISSN 2523-7667 Series E-ISSN 2523-7675
勋章
发表于 2025-3-30 06:59:51
Chao Chang,Junming Zhou,Weisheng Li,Zhengyang Wu,Jing Gao,Yong Tang