The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users
2017. The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users. In RecSys 2017 Poster Proceedings.and .
This poster presented our preliminary results on this project; the full paper will appear in the Conference on Fairness, Accountability, and Transparency.
Typical recommender evaluations treat users as an homogeneous unit. However, user subgroups often differ in their tastes, which can result more broadly in diverse recommender needs. Thus, these groups may have different degrees of satisfaction with the provided recommendations. We explore the offline top-N performance of collaborative filtering algorithms across two domains.
We find that several strategies achieve higher accuracy for dominant demographic groups, thus increasing the overall performance for the strategy, without providing increased benefits for other users.