Implicit feedback data is extensively explored in recommendation as it is easy to collect and generally applicable. However, predicting users' preference on implicit feedback data is a challenging task since we can only observe positive (voted) samples and unvoted samples. It is difficult to distinguish between the negative samples and unlabeled positive samples from the unvoted ones. Existing works, such as Bayesian Personalized Ranking (BPR), sample unvoted items as negative samples uniformly, therefore suffer from a critical noisy-label issue. To address this gap, we design an adaptive sampler based on noisy-label robust learning for implicit feedback data. To formulate the issue, we first introduce Bayesian Point-wise Optimization (BPO) to learn a model, e.g., Matrix Factorization (MF), by maximum likelihood estimation. We predict users' preferences with the model and learn it by maximizing likelihood of observed data labels, i.e., a user prefers her positive samples and has no interests in her unvoted samples. However, in reality, a user may have interests in some of her unvoted samples, which are indeed positive samples mislabeled as negative ones. We then consider the risk of these noisy labels, and propose a Noisy-label Robust BPO (NBPO). NBPO also maximizes the observation likelihood while connects users' preference and observed labels by the likelihood of label flipping based on the Bayes' theorem. In NBPO, a user prefers her true positive samples and shows no interests in her true negative samples, hence the optimization quality is dramatically improved. Extensive experiments on two public real-world datasets show the significant improvement of our proposed optimization methods.

中文翻译：

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基于噪声标签鲁棒学习的隐式反馈数据采样器设计

隐式反馈数据在推荐中被广泛探索，因为它易于收集且普遍适用。然而，预测用户对隐式反馈数据的偏好是一项具有挑战性的任务，因为我们只能观察正（投票）样本和未投票样本。很难从未投票的样本中区分负样本和未标记的正样本。现有的工作，例如贝叶斯个性化排名 (BPR)，将未投票的项目统一采样为负样本，因此存在严重的噪声标签问题。为了解决这个差距，我们设计了一个基于噪声标签鲁棒学习的自适应采样器，用于隐式反馈数据。为了表述这个问题，我们首先引入贝叶斯逐点优化 (BPO) 以通过最大似然估计来学习模型，例如矩阵分解 (MF)。我们用模型预测用户的偏好，并通过最大化观察到的数据标签的可能性来学习它，即用户更喜欢她的正样本并且对她的未投票样本没有兴趣。然而，实际上，用户可能对她的一些未投票的样本感兴趣，这些样本确实是被错误标记为负样本的正样本。然后我们考虑这些嘈杂标签的风险，并提出了一个嘈杂标签鲁棒 BPO（NBPO）。NBPO 还最大化观察可能性，同时通过基于贝叶斯定理的标签翻转的可能性将用户的偏好和观察到的标签连接起来。在NBPO中，用户更喜欢她的真正样本，而对她的真负样本没有兴趣，因此优化质量得到了显着提高。