  Collaborative filtering based recommendation learns users' preferences from
all users' historical behavior data, and has been popular to facilitate
decision making. R Recently, the fairness issue of recommendation has become
more and more essential. A recommender system is considered unfair when it does
not perform equally well for different user groups according to users'
sensitive attributes~(e.g., gender, race). Plenty of methods have been proposed
to alleviate unfairness by optimizing a predefined fairness goal or changing
the distribution of unbalanced training data. However, they either suffered
from the specific fairness optimization metrics or relied on redesigning the
current recommendation architecture. In this paper, we study how to improve
recommendation fairness from the data augmentation perspective. The
recommendation model amplifies the inherent unfairness of imbalanced training
data. We augment imbalanced training data towards balanced data distribution to
improve fairness. The proposed framework is generally applicable to any
embedding-based recommendation, and does not need to pre-define a fairness
metric. Extensive experiments on two real-world datasets clearly demonstrate
the superiority of our proposed framework. We publish the source code at
https://github.com/newlei/FDA.
