Partial Label Learning-Inspired Denoising Implicit Feedback for Recommendation
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.