  Recommender systems (RSs) have become an indispensable part of online
platforms. With the growing concerns of algorithmic fairness, RSs are not only
expected to deliver high-quality personalized content, but are also demanded
not to discriminate against users based on their demographic information.
However, existing RSs could capture undesirable correlations between sensitive
features and observed user behaviors, leading to biased recommendations. Most
fair RSs tackle this problem by completely blocking the influences of sensitive
features on recommendations. But since sensitive features may also affect user
interests in a fair manner (e.g., race on culture-based preferences),
indiscriminately eliminating all the influences of sensitive features
inevitably degenerate the recommendations quality and necessary diversities. To
address this challenge, we propose a path-specific fair RS (PSF-RS) for
recommendations. Specifically, we summarize all fair and unfair correlations
between sensitive features and observed ratings into two latent proxy
mediators, where the concept of path-specific bias (PS-Bias) is defined based
on path-specific counterfactual inference. Inspired by Pearl's minimal change
principle, we address the PS-Bias by minimally transforming the biased factual
world into a hypothetically fair world, where a fair RS model can be learned
accordingly by solving a constrained optimization problem. For the technical
part, we propose a feasible implementation of PSF-RS, i.e., PSF-VAE, with
weakly-supervised variational inference, which robustly infers the latent
mediators such that unfairness can be mitigated while necessary recommendation
diversities can be maximally preserved simultaneously. Experiments conducted on
semi-simulated and real-world datasets demonstrate the effectiveness of PSF-RS.
