Recommender systems suffer from confounding biases when there exist confounders affecting both item features and user feedback (e.g., like or not). Existing causal recommendation methods typically assume confounders are fully observed and measured, forgoing the possible existence of hidden confounders in real applications. For instance, product quality is a confounder since affecting both item prices and user ratings, but is hidden for the third-party e-commerce platform due to the difficulty of large-scale quality inspection; ignoring it could result in the bias effect of over-recommending high-price items. This work analyzes and addresses the problem from a causal perspective. The key lies in modeling the causal effect of item features on a user's feedback. To mitigate hidden confounding effects, it is compulsory but challenging to estimate the causal effect without measuring the confounder. Towards this goal, we propose a Hidden Confounder Removal (HCR) framework that leverages front-door adjustment to decompose the causal effect into two partial effects, according to the mediators between item features and user feedback. The partial effects are independent from the hidden confounder and identifiable. During training, HCR performs multi-task learning to infer the partial effects from historical interactions. We instantiate HCR for two scenarios and conduct experiments on three real-world datasets. Empirical results show that the HCR framework provides more accurate recommendations, especially for less-active users. We will release the code once accepted.

翻译：推荐系统在存在同时影响物品特征和用户反馈（例如喜欢与否）的混杂因素时，会遭受混杂偏差的困扰。现有的因果推荐方法通常假设混杂因素被完全观测和测量，忽视了实际应用中可能存在的隐藏混杂因素。例如，产品质量是一个混杂因素，因为它同时影响物品价格和用户评分，但对于第三方电子商务平台而言，由于大规模质量检测的困难，该因素是隐藏的；忽略它可能导致过度推荐高价物品的偏差效应。本研究从因果视角分析并解决了该问题。关键在于建模物品特征对用户反馈的因果效应。为了缓解隐藏混杂效应，必须在不对混杂因素进行测量的情况下估计因果效应，这既是必要的，也是具有挑战性的。为此，我们提出了一个隐藏混杂因素去除（HCR）框架，该框架利用前门调整，根据物品特征与用户反馈之间的中介变量，将因果效应分解为两个部分效应。这些部分效应独立于隐藏混杂因素且是可识别的。在训练过程中，HCR通过多任务学习从历史交互中推断部分效应。我们在两种场景下实例化了HCR，并在三个真实世界数据集上进行了实验。实证结果表明，HCR框架能够提供更准确的推荐，特别是对于活跃度较低的用户。一旦论文被接受，我们将发布代码。