西瓜
发表于 2025-3-30 09:12:02
https://doi.org/10.1007/978-94-009-6268-2hanism is used to aggregate the social influence of users on the target user and the correlative items’ influence on a given item. The final latent factors of user and item are combined to make a prediction. Experiments on three real-world recommender system datasets validate the effectiveness of GN
tic-douloureux
发表于 2025-3-30 12:22:36
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/263437.jpg
刻苦读书
发表于 2025-3-30 19:33:58
http://reply.papertrans.cn/27/2635/263437/263437_53.png
GENUS
发表于 2025-3-31 00:15:29
https://doi.org/10.1007/978-981-19-9542-2mmendation list is crucial for user-oriented applications. Many knowledge-based approaches combine graph neural networks with exploring node structural similarity, while paying little attention to semantically distinguishing potential user interests and item attributes. Therefore, personalized node
aneurysm
发表于 2025-3-31 03:27:10
http://reply.papertrans.cn/27/2635/263437/263437_55.png
inchoate
发表于 2025-3-31 06:34:50
César Fernández-de-las-Peñas,Kimberly Bensentworks (GNN) are widely applied to SBR due to their superiority on learning better item and session embeddings. However, existing GNN-based SBR models mainly leverage direct neighbors, lacking efficient utilization of multi-hop neighbors information. To address this issue, we propose a multi-head gr
Myosin
发表于 2025-3-31 10:56:03
http://reply.papertrans.cn/27/2635/263437/263437_57.png
感染
发表于 2025-3-31 14:43:56
Occlusal Diagnosis and Treatment of TMDems yet neglect tail ones, which are actually the focus of novel recommendation since they can provide more surprises for users and more profits for enterprises. Furthermore, current novelty oriented methods treat all users equally without considering their personal preference on popular or tail ite
Ondines-curse
发表于 2025-3-31 21:18:22
http://reply.papertrans.cn/27/2635/263437/263437_59.png
Neuropeptides
发表于 2025-3-31 23:42:49
Larry Wolford,Jacinto Fernandez Sanromanmethods focus on incorporating the semantic collaborative information of social friends. In this paper, we argue that the semantic strength of their friends is also influenced by the subnetwork structure of friendship groups, which had not been well addressed in social recommendation literature. We