  Modern personalized recommendation services often rely on user feedback,
either explicit or implicit, to improve the quality of services. Explicit
feedback refers to behaviors like ratings, while implicit feedback refers to
behaviors like user clicks. However, in the scenario of full-screen video
viewing experiences like Tiktok and Reels, the click action is absent,
resulting in unclear feedback from users, hence introducing noises in modeling
training. Existing approaches on de-noising recommendation mainly focus on
positive instances while ignoring the noise in a large amount of sampled
negative feedback. In this paper, we propose a meta-learning method to annotate
the unlabeled data from loss and gradient perspectives, which considers the
noises in both positive and negative instances. Specifically, we first propose
an Inverse Dual Loss (IDL) to boost the true label learning and prevent the
false label learning. Then we further propose an Inverse Gradient (IG) method
to explore the correct updating gradient and adjust the updating based on
meta-learning. Finally, we conduct extensive experiments on both benchmark and
industrial datasets where our proposed method can significantly improve AUC by
9.25% against state-of-the-art methods. Further analysis verifies the proposed
inverse learning framework is model-agnostic and can improve a variety of
recommendation backbones. The source code, along with the best hyper-parameter
settings, is available at this link:
https://github.com/Guanyu-Lin/InverseLearning.
