Diversity-Aware Recommendations for Social Justice? Exploring User Diversity and Fairness in Recommender Systems

Diversity and fairness are increasingly linked in the field of personalized recommendations. For instance, the diversification of items (”item diversity”) is considered key to fairness. Less attention has been paid to ”user diversity” and its implications for fairness. In this paper, I problematize the conceptualization and application of user diversity in recommender systems. I argue that the widespread understanding of user diversity as natural, value-neutral, and individual-level categories may accidentally compound historical injustice. To mitigate emerging biases, diversity dimensions need to be contextualized by mapping structural inequalities between users. The paper thus stresses the importance of paying attention to the structural context of diversity, whereas the context refers to political and social circumstances surrounding the user’s life. The paper makes three contributions: 1) It connects fairness to diversity literature in the field of recommender system, 2) it specifies the tension between item-side and user-side fairness by revealing a bias in the treatment of user diversity, 3) it proposes solutions to mitigate the bias by drawing on Black feminist and critical race theory. This is a draft of the accepted paper "Diversity-Aware Recommendations for Social Justice? Exploring User Diversity and Fairness in Recommender Systems" presented at UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, June 2021, Pages 404–410, https://doi.org/10.1145/3450614.3463293