Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship

Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of how individuals experience culture. 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 culture as an aggregate concept. And, although existing novelty, diversity, and fairness studies recommender systems are related to the broader social role of cultural content, they do not adequately center culture as a core concept. In this work, we introduce the 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. Taking movie recommendation as a case study, we empirically compare the performance of more than twenty recommendation algorithms using commonality and existing utility, diversity, novelty, and fairness metrics. Our results demonstrate that commonality captures a property of system behavior complementary to existing metrics. These results suggest the need for alternative, non-personalized interventions in recommender systems. In this way, commonality contributes to a growing body of scholarship developing ‘public good’ rationales for digital media and machine learning systems.

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