miracle
发表于 2025-3-25 04:12:24
http://reply.papertrans.cn/29/2845/284468/284468_21.png
转折点
发表于 2025-3-25 09:54:37
https://doi.org/10.1007/978-3-642-86621-0on encoder to encode each propagation. . then employs a propagation transformer module to make every propagation embedding interact and obtain the importance score of each propagation. . achieves the best performance on three real-world datasets. Further experiments show the propagation transformer
accomplishment
发表于 2025-3-25 12:28:43
http://reply.papertrans.cn/29/2845/284468/284468_23.png
规范要多
发表于 2025-3-25 16:45:59
n. To facilitate user representation learning under sparse labels and insufficient features, we further propose self-supervised training specifically tailored for social networks with weak information. In the second stage, the cascade representations are learned using the multi-head self-attention n
拖网
发表于 2025-3-25 22:55:18
Positionen zu Arbeit und Technik, initially constructs a bias matrix for each user and item, calculates bias scores, and removes them from the raw rating data. Subsequently, the debiased data is fed into a GNN to learn users’ genuine preferences. Last, it reasonably combines biases and preferences to make predictions. We performed
Glower
发表于 2025-3-26 01:51:41
Handbuch der allgemeinen Pathologieferent classes. Furthermore, to fully explore multi-scale graph features for alleviating label deficiencies, ORAL generates pseudo-labels by aligning and ensembling label estimations from multiple stacked prototypical attention networks. Extensive experiments on several benchmark datasets show the e
left-ventricle
发表于 2025-3-26 08:17:53
,The Era of the Pioneers (1882 – 1898),ctive approach called RAP, which employs a two-stage learning framework. Specifically, in the first stage, we construct a weighted bipartite graph to model interaction’s confidence-score, which effectively blocks the spread of noise information in GNN. Furthermore, in the second stage, RAP introduce
incredulity
发表于 2025-3-26 11:39:18
https://doi.org/10.1007/978-3-642-75757-0ring model training, which improves the construction of new edges for inactive users. Extensive experiments on real-world datasets demonstrate that LSIR achieves significant improvements of up to 129.58% on NDCG in inactive user recommendation. Our code is available at ..
直觉好
发表于 2025-3-26 12:48:43
http://reply.papertrans.cn/29/2845/284468/284468_29.png
重力
发表于 2025-3-26 18:27:25
http://reply.papertrans.cn/29/2845/284468/284468_30.png