Disparate Impact in Item Recommendation: A Case of Geographic Imbalance

Gomez E.;Boratto L.;Salamo M.

2021-01-01

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Abstract

Recommender systems are key tools to push items’ consumption. Imbalances in the data distribution can affect the exposure given to providers, thus affecting their experience in online platforms. To study this phenomenon, we enrich two datasets and characterize data imbalance w.r.t. the country of production of an item (geographic imbalance). We focus on movie and book recommendation, and divide items into two classes based on their country of production, in a majority-versus-rest setting. To assess if recommender systems generate a disparate impact and (dis)advantage a group, we introduce metrics to characterize the visibility and exposure a group receives in the recommendations. Then, we run state-of-the-art recommender systems and measure the visibility and exposure given to each group. Results show the presence of a disparate impact that mostly favors the majority; however, factorization approaches are still capable of capturing the preferences for the minority items, thus creating a positive impact for the group. To mitigate disparities, we propose an approach to reach the target visibility and exposure for the disadvantaged group, with a negligible loss in effectiveness.