Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usually design each specific model for each specific confounder. However, real-world systems may include a huge number of confounders and thus designing each specific model for each specific confounder could be unrealistic. More importantly, except for those ``explicit confounders'' that experts can manually identify and process such as item's position in the ranking list, there are also many ``latent confounders'' that are beyond the imagination of experts. For example, users' rating on a song may depend on their current mood or the current weather, and users' preference on ice creams may depend on the air temperature. Such latent confounders may be unobservable in the recorded training data. To solve the problem, we propose Deconfounded Causal Collaborative Filtering (DCCF). We first frame user behaviors with unobserved confounders into a causal graph, and then we design a front-door adjustment model carefully fused with machine learning to deconfound the influence of unobserved confounders. Experiments on real-world datasets show that our method is able to deconfound unobserved confounders to achieve better recommendation performance.

翻译：推荐系统可能受到多种混杂因素（也称为混淆变量）的干扰，这些因素可能导致推荐不准确并降低推荐性能。当前解决该问题的方法通常为每种特定混杂变量设计专用模型。然而，现实世界系统可能包含大量混杂变量，因此为每种混杂变量设计专用模型并不现实。更重要的是，除了专家能够手动识别并处理的"显式混杂变量"（如物品在排序列表中的位置）外，还存在许多超出专家想象的"潜在混杂变量"。例如，用户对歌曲的评分可能取决于其当前情绪或天气状况，而用户对冰淇淋的偏好可能取决于气温。这类潜在混杂变量在记录的训练数据中可能无法观测。为解决该问题，我们提出去混杂因果协同过滤（Deconfounded Causal Collaborative Filtering, DCCF）。我们首先将存在未观测混杂变量的用户行为建模为因果图，随后设计一个与机器学习精心融合的前门调整模型，以消除未观测混杂变量的影响。在真实数据集上的实验表明，我们的方法能够有效消除未观测混杂变量，从而实现更优的推荐性能。