  We explore the fairness issue that arises in recommender systems. Biased data
due to inherent stereotypes of particular groups (e.g., male students' average
rating on mathematics is often higher than that on humanities, and vice versa
for females) may yield a limited scope of suggested items to a certain group of
users. Our main contribution lies in the introduction of a novel fairness
notion (that we call equal experience), which can serve to regulate such
unfairness in the presence of biased data. The notion captures the degree of
the equal experience of item recommendations across distinct groups. We propose
an optimization framework that incorporates the fairness notion as a
regularization term, as well as introduce computationally-efficient algorithms
that solve the optimization. Experiments on synthetic and benchmark real
datasets demonstrate that the proposed framework can indeed mitigate such
unfairness while exhibiting a minor degradation of recommendation accuracy.
