Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most of the existing methods require some specific data, architectures and training methods. In this paper, we first argue through an online study that arbitrarily removing all the biases in industrial recommendation may not consistently yield a desired performance improvement. For the situation that a randomized dataset is not available, we propose a novel self-sampling training and evaluation (SSTE) framework to achieve the accuracy-bias tradeoff in recommendation, i.e., eliminate the harmful biases and preserve the beneficial ones. Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data. A self-training module infers the beneficial biases and learns better tradeoff based on these subsets, and a self-evaluation module aims to use these subsets to construct more plausible references to reflect the optimized model. Finally, we conduct extensive offline experiments on two datasets to verify the effectiveness of our SSTE. Moreover, we deploy our SSTE in homepage recommendation of a famous financial management product called Tencent Licaitong, and find very promising results in an online A/B test.

翻译：有关去偏推荐的研究已取得显著进展，但在工业推荐场景中实际应用仍需解决若干问题。例如，现有的大多数方法需要特定的数据、架构和训练方法。本文首先通过在线实验论证：在工业推荐中盲目消除所有偏差并不总能带来预期的性能提升。针对缺乏随机化数据集的实际情况，我们提出一种全新的自采样训练与评估框架（SSTE），以实现推荐系统中准确性-偏差的权衡——即消除有害偏差，保留有益偏差。具体而言，SSTE通过自采样模块从原始训练集和验证集中生成具有不同偏差程度的子集；自训练模块基于这些子集推断有益偏差并学习更优的权衡策略；自评估模块则利用这些子集构建更合理的参照标准以反映优化后的模型。最后，我们在两个数据集上进行了大量离线实验验证SSTE的有效性，并将其部署于知名金融理财产品"腾讯理财通"的首页推荐系统，在线A/B测试中取得了令人满意的结果。