  Sequential recommendation (SR) learns from the temporal dynamics of user-item
interactions to predict the next ones. Fairness-aware recommendation mitigates
a variety of algorithmic biases in the learning of user preferences. This paper
aims at bringing a marriage between SR and algorithmic fairness. We propose a
novel fairness-aware sequential recommendation task, in which a new metric,
interaction fairness, is defined to estimate how recommended items are fairly
interacted by users with different protected attribute groups. We propose a
multi-task learning based deep end-to-end model, FairSR, which consists of two
parts. One is to learn and distill personalized sequential features from the
given user and her item sequence for SR. The other is fairness-aware preference
graph embedding (FPGE). The aim of FPGE is two-fold: incorporating the
knowledge of users' and items' attributes and their correlation into entity
representations, and alleviating the unfair distributions of user attributes on
items. Extensive experiments conducted on three datasets show FairSR can
outperform state-of-the-art SR models in recommendation performance. In
addition, the recommended items by FairSR also exhibit promising interaction
fairness.
