Historical interactions are the default choice for recommender model training, which typically exhibit high sparsity, i.e., most user-item pairs are unobserved missing data. A standard choice is treating the missing data as negative training samples and estimating interaction likelihood between user-item pairs along with the observed interactions. In this way, some potential interactions are inevitably mislabeled during training, which will hurt the model fidelity, hindering the model to recall the mislabeled items, especially the long-tail ones. In this work, we investigate the mislabeling issue from a new perspective of aleatoric uncertainty, which describes the inherent randomness of missing data. The randomness pushes us to go beyond merely the interaction likelihood and embrace aleatoric uncertainty modeling. Towards this end, we propose a new Aleatoric Uncertainty-aware Recommendation (AUR) framework that consists of a new uncertainty estimator along with a normal recommender model. According to the theory of aleatoric uncertainty, we derive a new recommendation objective to learn the estimator. As the chance of mislabeling reflects the potential of a pair, AUR makes recommendations according to the uncertainty, which is demonstrated to improve the recommendation performance of less popular items without sacrificing the overall performance. We instantiate AUR on three representative recommender models: Matrix Factorization (MF), LightGCN, and VAE from mainstream model architectures. Extensive results on two real-world datasets validate the effectiveness of AUR w.r.t. better recommendation results, especially on long-tail items.

翻译：历史交互是推荐模型训练的默认选择，这些交互通常具有高度稀疏性，即大多数用户-物品对属于未观测到的缺失数据。标准做法是将缺失数据视为负训练样本，并与观测到的交互一起估计用户-物品对之间的交互可能性。这种方法在训练过程中不可避免地会错误标记某些潜在交互，从而损害模型保真度，阻碍模型召回被错误标记的物品，尤其是长尾物品。在本工作中，我们从任意不确定性的新视角研究误标问题，该不确定性描述了缺失数据固有的随机性。这种随机性促使我们超越仅关注交互可能性的范畴，将任意不确定性建模纳入考虑。为此，我们提出了一种新的任意不确定性感知推荐（AUR）框架，该框架包含一个不确定性估计器和一个标准推荐模型。根据任意不确定性理论，我们推导出新的推荐目标函数以学习该估计器。由于误标概率反映了用户-物品对的潜在可能性，AUR根据不确定性进行推荐，这被证明能在不牺牲整体性能的前提下提升冷门物品的推荐效果。我们在三种代表性推荐模型（矩阵分解（MF）、LightGCN和VAE）上实例化AUR，这些模型来自主流架构。在两个真实数据集上的广泛结果验证了AUR在推荐效果（尤其是长尾物品）上的有效性。