Title: Inverse Learning with Extremely Sparse Feedback for Recommendation. Abstract: Negative sampling is widely used in modern recommender systems, where negative data is randomly sampled from the whole item pool. However, such a strategy often introduces false-positive noises. Existing approaches about de-noising recommendation mainly focus on positive instances while ignoring the noise in the 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 on both positive and negative instances. Specifically, we first propose $\textit{inverse dual loss}$ (IDL) to boost the true label learning and prevent the false label learning, based on the loss of unlabeled data towards true and false labels during the training process. To achieve more robust sampling on hard instances, we further propose $\textit{inverse gradient}$ (IG) to explore the correct updating gradient and adjust the updating based on meta learning. We conduct extensive experiments on a benchmark and an industrially collected dataset where our proposed method can significantly improve AUC by $9.25\%$ against state-of-the-art methods. Further analysis verifies the proposed inverse learning is model-agnostic and can well annotate the labels combined with different recommendation backbones. The source code along with the best hyper-parameter settings is available at this link: https://anonymous.4open.science/r/InverseLearning-4F4F.
