Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance. In contrast, unbiased ratings obtained from randomized controlled trials or A/B tests are considered to be the golden standard, but are costly and small in scale in reality. To exploit both types of data, recent works proposed to use unbiased ratings to correct the parameters of the propensity or imputation models trained on the biased dataset. However, the existing methods fail to obtain accurate predictions in the presence of unobserved confounding or model misspecification. In this paper, we propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method with the aim of combating unobserved confounding and model misspecification. The proposed approach makes full use of unbiased data by alternatively correcting model parameters learned with biased data, and adaptively learning balance coefficients of biased samples for further debiasing. Extensive real-world experiments are conducted along with the deployment of our proposal on four representative debiasing methods to demonstrate the effectiveness.
翻译:推荐系统被视为解决信息过载的有效工具,但众所周知,各种偏差的存在使得直接在大规模观测数据上进行训练导致次优的预测性能。相比之下,随机对照试验或A/B测试获得的无偏评级被认为是黄金标准,但在现实中成本高昂且规模较小。为了利用两种类型的数据,最近的研究提出了使用无偏评级来纠正针对偏置数据集训练的倾向或插补模型的参数的方法。然而,现有方法在存在未观测混淆或模型错误规范时不能获得准确的预测结果。在本文中,我们提出了一种理论上有保证的模型不可知平衡方法,可以应用于任何现有的去偏斜方法,以应对未观测的混淆和模型错误规范。所提出的方法充分利用了无偏数据,通过交替纠正针对偏置数据学习的模型参数,并自适应学习偏倚样本的平衡系数以进一步去除偏差。进行了广泛的实际世界实验,并部署我们的提案在四种代表性的去偏斜方法上以证明其有效性。