Top-N group recommendations with fairness
Dimitris Sacharidis · 2019
In many settings it is required that items are recommended to a group of users instead of a single user. Conventionally, the objective is to maximize the overall satisfaction among group members. Recently, however, attention has shifted to ensuring that recommendations are fair in that they should minimize the feeling of dissatisfaction among members. In this work, we explore a simple but intuitive notion of fairness: the minimum utility a group member receives. We propose a technique that seeks to rank the Pareto, or unanimously, optimal items by considering all admissible ways in which a group might reach a decision. As our detailed experimental study shows, this results in top-N recommendations that not only achieve a high minimum utility compared to other fairness-aware techniques, but also a high average utility across all group members beating standard aggregation strategies.