  Fairness in recommendation has attracted increasing attention due to bias and
discrimination possibly caused by traditional recommenders. In Interactive
Recommender Systems (IRS), user preferences and the system's fairness status
are constantly changing over time. Existing fairness-aware recommenders mainly
consider fairness in static settings. Directly applying existing methods to IRS
will result in poor recommendation. To resolve this problem, we propose a
reinforcement learning based framework, FairRec, to dynamically maintain a
long-term balance between accuracy and fairness in IRS. User preferences and
the system's fairness status are jointly compressed into the state
representation to generate recommendations. FairRec aims at maximizing our
designed cumulative reward that combines accuracy and fairness. Extensive
experiments validate that FairRec can improve fairness, while preserving good
recommendation quality.
