arXiv 2006.041532020-06cs.IR

隐式反馈推荐的去噪方法

Denoising Implicit Feedback for Recommendation

Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua

发表信息：: 2021. In Proceedings of the Fourteenth ACM International Conference on Web Search and Data Mining (WSDM'21)
DOI：: 10.1145/3437963.3441800

摘要：

普遍存在的隐式反馈使其成为构建在线推荐系统的默认选择。虽然大量的隐式反馈消除了数据稀疏性问题，但缺点是它们在反映用户的实际满意度方面并不干净。例如，在电子商务中，很大一部分点击并没有转化为购买，许多购买最终都得到了负面评价。因此，它是至关重要的，以考虑到不可避免的噪声在隐式反馈的推荐训练。然而，很少有工作建议已考虑到噪音的性质隐式反馈。在这项工作中，我们探讨了去噪隐式反馈推荐训练的中心主题。我们发现嘈杂的隐式反馈的严重负面影响，即，拟合噪声数据阻止了推荐器学习实际的用户偏好。我们的目标是识别和修剪噪声的相互作用，从而提高推荐训练的质量。通过观察正常推荐训练的过程，我们发现，噪声反馈通常在早期阶段具有较大的损失值。受此启发，我们提出了一种新的训练策略，称为自适应去噪训练（ADT），它自适应地修剪训练过程中的噪声相互作用。具体来说，我们设计了两种自适应损失公式化的范例：截断损失，丢弃大损失的样本在每次迭代中的动态阈值;和重加权损失，自适应地降低大损失样本的权重。我们在广泛使用的二进制交叉熵损失上实例化了这两种范例，并在三个有代表性的机器上测试了所提出的ADT策略。在三个基准测试上的大量实验表明，ADT显著提高了推荐的质量。

英文摘要：

The ubiquity of implicit feedback makes them the default choice to build online recommender systems. While the large volume of implicit feedback alleviates the data sparsity issue, the downside is that they are not as clean in reflecting the actual satisfaction of users. For example, in E-commerce, a large portion of clicks do not translate to purchases, and many purchases end up with negative reviews. As such, it is of critical importance to account for the inevitable noises in implicit feedback for recommender training. However, little work on recommendation has taken the noisy nature of implicit feedback into consideration. In this work, we explore the central theme of denoising implicit feedback for recommender training. We find serious negative impacts of noisy implicit feedback,i.e., fitting the noisy data prevents the recommender from learning the actual user preference. Our target is to identify and prune noisy interactions, so as to improve the quality of recommender training. By observing the process of normal recommender training, we find that noisy feedback typically has large loss values in the early stages. Inspired by this observation, we propose a new training strategy namedAdaptive Denoising Training(ADT), which adaptively prunes noisy interactions during training. Specifically, we devise two paradigms for adaptive loss formulation: Truncated Loss that discards the large-loss samples with a dynamic threshold in each iteration; and reweighted Loss that adaptively lowers the weight of large-loss samples. We instantiate the two paradigms on the widely used binary cross-entropy loss and test the proposed ADT strategies on three representative recommenders. Extensive experiments on three benchmarks demonstrate that ADT significantly improves the quality of recommendation over normal training.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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