  News recommendation aims to help online news platform users find their
preferred news articles. Existing news recommendation methods usually learn
models from historical user behaviors on news. However, these behaviors are
usually biased on news providers. Models trained on biased user data may
capture and even amplify the biases on news providers, and are unfair for some
minority news providers. In this paper, we propose a provider fairness-aware
news recommendation framework (named ProFairRec), which can learn news
recommendation models fair for different news providers from biased user data.
The core idea of ProFairRec is to learn provider-fair news representations and
provider-fair user representations to achieve provider fairness. To learn
provider-fair representations from biased data, we employ provider-biased
representations to inherit provider bias from data. Provider-fair and -biased
news representations are learned from news content and provider IDs
respectively, which are further aggregated to build fair and biased user
representations based on user click history. All of these representations are
used in model training while only fair representations are used for user-news
matching to achieve fair news recommendation. Besides, we propose an
adversarial learning task on news provider discrimination to prevent
provider-fair news representation from encoding provider bias. We also propose
an orthogonal regularization on provider-fair and -biased representations to
better reduce provider bias in provider-fair representations. Moreover,
ProFairRec is a general framework and can be applied to different news
recommendation methods. Extensive experiments on a public dataset verify that
our ProFairRec approach can effectively improve the provider fairness of many
existing methods and meanwhile maintain their recommendation accuracy.
