Temporal Domain Generalization (TDG) aims to learn from historical domains and generalize to unseen future distributions under concept drift. Nevertheless, prevailing TDG methods struggle with complex real-world streaming scenarios involving both multi-scale drift patterns (e.g., long-term periodicity intertwined with short-term incremental changes) and local uncertainties, especially in continuous settings where observations arrive irregularly. To address this limitation, we propose FreKoo++, a novel continuous spectral-dynamical framework that pioneers the unification of continuous Koopman modal dynamics with adaptive spectral disentanglement. Specifically, FreKoo++ maps source-domain parameters into a compact latent space, modeling their evolution as a superposition of learnable continuous modes where complex eigenvalues jointly encode oscillatory frequency and temporal growth or decay. This formulation naturally accommodates irregular timestamps and supports arbitrary horizon extrapolation without rigid discrete stepping. Furthermore, we propose a new adaptive soft spectral weighting mechanism backed by stability and spectral regularization, which automatically isolates persistent dominant dynamics from transient noise without relying on manual frequency thresholds. We derive modal approximation and generalization bounds that characterize how amplitude and eigenvalue estimation errors propagate with the prediction horizon. Extensive experiments on both discrete and continuous TDG benchmarks demonstrate that FreKoo++ achieves state-of-the-art performance under complex multi-scale drifts and irregular sampling.
Enshui Yu, Xiaoyu Yang, Wei Duan et al.· 0 citations
Implicit feedback, such as clicks and browsing behaviors, is ubiquitous in recommender systems as a proxy for user preferences. However, these signals are inherently noisy; interactions such as misclicks, unintended views, or unsatisfactory purchases often introduce false positive patterns that mislead model learning. Existing denoising strategies mainly rely on heuristic ''small-loss'' principles to suppress the influence of high-loss samples. However, this approach creates a fundamental trade-off: by indiscriminately penalizing large-value losses, these methods inadvertently weaken the model's ability to learn ''hard'' true positive interactions, thereby compromising robustness and personalization. By conceptualizing this ambiguity as a ''candidate label set'' that encompasses both true and noisy feedback, we are the first to reformulate the recommendation denoising problem into a Partial Label Learning (PLL) task. This novel perspective allows us to address the fundamental challenge of unreliable pseudo-labels by transforming traditional heuristic-based filtering into a principled label disambiguation process. Specifically, we propose PLLD, a Partial Label Learning-inspired Denoising method. Unlike existing methods that rely on indirect signal filtering, PLLD innovatively leverages PLL paradigms to directly resolve ambiguous implicit feedback, effectively recovering clean signals from noisy candidate sets. Experiments on multiple real-world benchmark datasets demonstrate that PLLD consistently improves ranking performance and robustness under substantial noise.
Huilin Chen, Jie Lu, Kezhi Lu et al.· Annual International ACM SIG...· 0 citations