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Sequential recommendation (SR) learns from the temporal dynamics of user-iteminteractions to predict the next ones. Fairness-aware recommendation mitigatesa variety of algorithmic biases in the learning of user preferences. This paperaims at bringing a marriage between SR and algorithmic fairness. We propose anovel fairness-aware sequential recommendation task, in which a new metric,interaction fairness, is defined to estimate how recommended items are fairlyinteracted by users with different protected attribute groups. We propose amulti-task learning based deep end-to-end model, FairSR, which consists of twoparts. One is to learn and distill personalized sequential features from thegiven user and her item sequence for SR. The other is fairness-aware preferencegraph embedding (FPGE). The aim of FPGE is two-fold: incorporating theknowledge of users' and items' attributes and their correlation into entityrepresentations, and alleviating the unfair distributions of user attributes onitems. Extensive experiments conducted on three datasets show FairSR canoutperform state-of-the-art SR models in recommendation performance. Inaddition, the recommended items by FairSR also exhibit promising interactionfairness.

FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings