Cross-domain recommendation (CDR) uses auxiliary interactions from a source domain to mitigate data sparsity in a target domain. However, implicit feedback in the source domain may include false positives that distort user representations and transfer misleading preferences to the target domain. We refer to this proble...
Guo-Hang Zeng, Jie Lu, Guang-Quan Zhang· Proceedings of the 20th ACM...· 0 citations
BACKGROUND AND OBJECTIVE
Ischemic stroke is a leading cause of mortality and long-term disability worldwide. Genetic factors contribute to IS susceptibility, yet conventional polygenic risk score approaches are primarily based on additive effects and may not fully capture non-linear relationships or positional context...
Yue Yang, Kai-Rui Guo, Zhen Fang et al.· Computer Methods and Program...· 0 citations
FreKoo++ is proposed, a novel continuous spectral-dynamical framework that pioneers the unification of continuous Koopman modal dynamics with adaptive spectral disentanglement and derives modal approximation and generalization bounds that characterize how amplitude and eigenvalue estimation errors propagate with the pr...
En-Shui Yu, Xiao-Yu Yang, Wei Duan et al.· 0 citations
This work is the first to reformulate the recommendation denoising problem into a Partial Label Learning (PLL) task, and innovatively leverages PLL paradigms to directly resolve ambiguous implicit feedback, effectively recovering clean signals from noisy candidate sets.
Huilin Chen, Jie Lu, Kezhi Lu et al.· Annual International ACM SIG...· 0 citations
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