  Recommendation fairness has attracted great attention recently. In real-world
systems, users usually have multiple sensitive attributes (e.g. age, gender,
and occupation), and users may not want their recommendation results influenced
by those attributes. Moreover, which of and when these user attributes should
be considered in fairness-aware modeling should depend on users' specific
demands. In this work, we define the selective fairness task, where users can
flexibly choose which sensitive attributes should the recommendation model be
bias-free. We propose a novel parameter-efficient prompt-based fairness-aware
recommendation (PFRec) framework, which relies on attribute-specific
prompt-based bias eliminators with adversarial training, enabling selective
fairness with different attribute combinations on sequential recommendation.
Both task-specific and user-specific prompts are considered. We conduct
extensive evaluations to verify PFRec's superiority in selective fairness. The
source codes are released in \url{https://github.com/wyqing20/PFRec}.
