  Recommender systems have become the dominant means of curating cultural
content, significantly influencing individual cultural experience. Since
recommender systems tend to optimize for personalized user experience, they can
overlook impacts on cultural experience in the aggregate. After demonstrating
that existing metrics do not center culture, we introduce a new metric,
commonality, that measures the degree to which recommendations familiarize a
given user population with specified categories of cultural content. We
developed commonality through an interdisciplinary dialogue between researchers
in computer science and the social sciences and humanities. With reference to
principles underpinning public service media systems in democratic societies,
we identify universality of address and content diversity in the service of
strengthening cultural citizenship as particularly relevant goals for
recommender systems delivering cultural content. We develop commonality as a
measure of recommender system alignment with the promotion of content toward a
shared cultural experience across a population of users. We empirically compare
the performance of recommendation algorithms using commonality with existing
metrics, demonstrating that commonality captures a novel property of system
behavior complementary to existing metrics. Alongside existing fairness and
diversity metrics, commonality contributes to a growing body of scholarship
developing `public good' rationales for machine learning systems.
