Abstract
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.
Original language | English |
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Journal | CEUR Workshop Proceedings |
Volume | 1905 |
Publication status | Published - 2017 |
Externally published | Yes |
Event | 2017 Poster Track of the 11th ACM Conference on Recommender Systems, Poster-Recsys 2017 - Como, Italy Duration: 28 Aug 2017 → 28 Aug 2017 |
Keywords
- Collaborative filtering
- Evaluation popularity bias