  Single-tower models are widely used in the ranking stage of news
recommendation to accurately rank candidate news according to their
fine-grained relatedness with user interest indicated by user behaviors.
However, these models can easily inherit the biases related to users' sensitive
attributes (e.g., demographics) encoded in training click data, and may
generate recommendation results that are unfair to users with certain
attributes. In this paper, we propose FairRank, which is a fairness-aware
single-tower ranking framework for news recommendation. Since candidate news
selection can be biased, we propose to use a shared candidate-aware user model
to match user interest with a real displayed candidate news and a random news,
respectively, to learn a candidate-aware user embedding that reflects user
interest in candidate news and a candidate-invariant user embedding that
indicates intrinsic user interest. We apply adversarial learning to both of
them to reduce the biases brought by sensitive user attributes. In addition, we
use a KL loss to regularize the attribute labels inferred from the two user
embeddings to be similar, which can make the model capture less candidate-aware
bias information. Extensive experiments on two datasets show that FairRank can
improve the fairness of various single-tower news ranking models with minor
performance losses.
