Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural Citizenship

Abstract

Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience. While the majority of academic and industrial 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 recommender 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 of recommender systems 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 public service media (PSM) 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 a shared cultural experience of, and exposure to, diverse cultural content across a population of users. Moreover, we advocate for the involvement of human editors accountable to a larger value community as a fundamental part of defining categories in the service of cultural citizenship. We empirically compare the performance of recommendation algorithms using commonality with existing utility, diversity, novelty, and fairness metrics using three different domains. Our results demonstrate that commonality captures a property of system behavior complementary to existing metrics and suggests the need for alternative, non-personalized interventions in recommender systems oriented to strengthening cultural citizenship across populations of users. Moreover, commonality demonstrates both consistent results under different editorial policies and robustness to missing labels and users. Alongside existing fairness and diversity metrics, commonality contributes to a growing body of scholarship developing “public good” rationales for digital media and machine learning systems.