# name
Provider fairness across continents in collaborative recommender systems

# metadata.dc.description.abstract[].value
When a recommender system suggests items to the end-users, it gives a certain exposure to the providers behind the recommended items. Indeed, the system offers a possibility to the items of those providers of being reached and consumed by the end-users. Hence, according to how recommendation lists are shaped, the experience of under-recommended providers in online platforms can be affected. To study this phenomenon, we focus on movie and book recommendation and enrich two datasets with the continent of production of an item. We use this data to characterize imbalances in the distribution of the user-item observations and regarding where items are produced (geographic imbalance). To assess if recommender systems generate a disparate impact and (dis)advantage a group, we divide items into groups, based on their continent of production, and characterize how represented is each group in the data. Then, we run state-of-the-art recommender systems and measure the visibility and exposure given to each group. We observe disparities that favor the most represented groups. We overcome these phenomena by introducing equity with a re-ranking approach that regulates the share of recommendations given to the items produced in a continent (visibility) and the positions in which items are ranked in the recommendation list (exposure), with a negligible loss in effectiveness, thus controlling fairness of providers coming from different continents. A comparison with the state of the art shows that our approach can provide more equity for providers, both in terms of visibility and of exposure.

# metadata.dc.relation.ispartof[].value
Information Processing & Management, 2022, vol. 59, num. 1

# metadata.dc.source[].value
Articles publicats en revistes (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Provider fairness across continents in collaborative recommender systems