  Recommender systems are gaining increasing and critical impacts on human and
society since a growing number of users use them for information seeking and
decision making. Therefore, it is crucial to address the potential unfairness
problems in recommendations. Just like users have personalized preferences on
items, users' demands for fairness are also personalized in many scenarios.
Therefore, it is important to provide personalized fair recommendations for
users to satisfy their personalized fairness demands. Besides, previous works
on fair recommendation mainly focus on association-based fairness. However, it
is important to advance from associative fairness notions to causal fairness
notions for assessing fairness more properly in recommender systems. Based on
the above considerations, this paper focuses on achieving personalized
counterfactual fairness for users in recommender systems. To this end, we
introduce a framework for achieving counterfactually fair recommendations
through adversary learning by generating feature-independent user embeddings
for recommendation. The framework allows recommender systems to achieve
personalized fairness for users while also covering non-personalized
situations. Experiments on two real-world datasets with shallow and deep
recommendation algorithms show that our method can generate fairer
recommendations for users with a desirable recommendation performance.
