In spite of the tremendous development of recommender system owing to the
progressive capability of machine learning recently, the current recommender
system is still vulnerable to the distribution shift of users and items in
realistic scenarios, leading to the sharp decline of performance in testing
environments. It is even more severe in many common applications where only the
implicit feedback from sparse data is available. Hence, it is crucial to
promote the performance stability of recommendation method in different
environments. In this work, we first make a thorough analysis of implicit
recommendation problem from the viewpoint of out-of-distribution (OOD)
generalization. Then under the guidance of our theoretical analysis, we propose
to incorporate the recommendation-specific DAG learner into a novel causal
preference-based recommendation framework named CausPref, mainly consisting of
causal learning of invariant user preference and anti-preference negative
sampling to deal with implicit feedback. Extensive experimental results from
real-world datasets clearly demonstrate that our approach surpasses the
benchmark models significantly under types of out-of-distribution settings, and
show its impressive interpretability.

本文提出了一种基于因果偏好的推荐框架 CausPref，该框架利用推荐特定 DAG 学习器进行因果学习，在处理隐式反馈过程中运用反偏好负采样技术，并在类型不同的超出分布情境下显著超越了基准模型。