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 is unrealistic. More
importantly, except for those "explicit confounders" that researchers 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
researchers. 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 a deconfounded causal
collaborative filtering model. 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. The proposed model is able to handle both global
confounders and personalized confounders. Experiments on real-world e-commerce
datasets show that our method is able to deconfound unobserved confounders to
achieve better recommendation performance.

提出了一个去混淆因素的因果协作过滤模型，解决了现实场景下推荐系统可能遇到的无法观测到的混淆因素带来的问题，且在真实数据集上的实验证明了该方法能够更好地提高推荐效果。