臆断
发表于 2025-3-23 11:53:59
Query2Trip: Dual-Debiased Learning for Neural Trip Recommendationhe query provided by a user, Query2Trip designs a debiased adversarial learning module by conditional guidance to alleviate this selection bias from positives (visited). The latter happens as unvisited is not equivalent to negative. Query2Trip devises a debiased contrastive learning module by negati
溃烂
发表于 2025-3-23 16:06:26
A New Reconstruction Attack: User Latent Vector Leakage in Federated Recommendationgenerator is designed to take random vectors as inputs and outputs generated latent vectors. The generator is trained by the distance between the real victim’s gradient updates and the generated gradient updates. We explain that the generator will successfully learn the target latent vector distribu
ROOF
发表于 2025-3-23 21:13:37
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跑过
发表于 2025-3-23 22:34:44
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Presbyopia
发表于 2025-3-24 04:06:36
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华而不实
发表于 2025-3-24 10:28:21
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抚慰
发表于 2025-3-24 14:35:09
Intention-Aware User Modeling for Personalized News Recommendationerence for personalized next-news recommendations. In addition to modeling users’ reading preferences, our proposed model IPNR can also capture users’ reading intentions and the transitions over intentions for better predicting the next piece of news which may interest the user. Extensive experiment
有权
发表于 2025-3-24 15:13:20
Deep User and Item Inter-matching Network for CTR Prediction by users’ historical behaviors, respectively. Then the User-to-User Network (UUN) is designed to mine user interests through the relationship between target users and similar users after representing the target users more accurately and richly. The experimental results show that the DUIIN model pro
ineptitude
发表于 2025-3-24 21:50:31
Towards Lightweight Cross-Domain Sequential Recommendation via External Attention-Enhanced Graph Coniently to capture the collaborative filtering signals of the items from both domains. To further alleviate the framework structure and aggregate the user-specific sequential pattern, we devise a novel dual-channel External Attention (EA) component, which calculates the correlation among all items vi
下船
发表于 2025-3-24 23:16:34
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