  Recommender systems have become the dominant means of curating cultural
content, significantly influencing the nature of individual cultural
experience. While the majority of research on recommender systems optimizes for
personalized user experience, this paradigm does not capture the ways that
recommender systems impact cultural experience in the aggregate, across
populations of users. Although existing novelty, diversity, and fairness
studies probe how systems relate to the broader social role of cultural
content, they do not adequately center culture as a core concept and challenge.
In this work, we introduce commonality as a new measure that reflects the
degree to which recommendations familiarize a given user population with
specified categories of cultural content. Our proposed commonality metric
responds to a set of arguments developed through an interdisciplinary dialogue
between researchers in computer science and the social sciences and humanities.
With reference to principles underpinning non-profit, 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. Taking diversity in movie recommendation as a case study in enhancing
pluralistic cultural experience, we empirically compare systems' performance
using commonality and existing utility, diversity, and fairness metrics. Our
results demonstrate that commonality captures a property of system behavior
complementary to existing metrics and suggest the need for alternative,
non-personalized interventions in recommender systems oriented to strengthening
cultural citizenship across populations of users. In this way, commonality
contributes to a growing body of scholarship developing 'public good'
rationales for digital media and ML systems.
