  The increasing role of recommender systems in many aspects of society makes
it essential to consider how such systems may impact social good. Various
modifications to recommendation algorithms have been proposed to improve their
performance for specific socially relevant measures. However, previous
proposals are often not easily adapted to different measures, and they
generally require the ability to modify either existing system inputs, the
system's algorithm, or the system's outputs. As an alternative, in this paper
we introduce the idea of improving the social desirability of recommender
system outputs by adding more data to the input, an approach we view as
providing `antidote' data to the system. We formalize the antidote data
problem, and develop optimization-based solutions. We take as our model system
the matrix factorization approach to recommendation, and we propose a set of
measures to capture the polarization or fairness of recommendations. We then
show how to generate antidote data for each measure, pointing out a number of
computational efficiencies, and discuss the impact on overall system accuracy.
Our experiments show that a modest budget for antidote data can lead to
significant improvements in the polarization or fairness of recommendations.
