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.

翻译：现代个性化推荐服务通常依赖用户反馈（显式或隐式）来提升服务质量。显式反馈指评分等行为，隐式反馈则包括用户点击。然而，在TikTok和Reels等全屏视频观看场景中，点击行为缺失导致用户反馈模糊，从而在模型训练中引入噪声。现有推荐去噪方法主要关注正样本，却忽略了大量采样负反馈中的噪声。本文提出一种元学习方法，通过损失函数与梯度视角对未标注数据进行标注，同时兼顾正负样本中的噪声。具体而言，我们首先提出逆向双损失（IDL）以增强真实标签学习并抑制虚假标签学习，进而提出逆向梯度（IG）方法探索正确更新梯度并通过元学习调整更新过程。在基准数据集与工业数据集上的大量实验表明，所提方法相较于现有最优方法AUC提升9.25%。进一步分析验证了该逆向学习框架具有模型无关性，可有效改进多种推荐基干模型。源代码及最优超参数设置已开源：https://github.com/Guanyu-Lin/InverseLearning。