Name
Abstract | ||
|---|---|---|
Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendation services, the interests of those providers should be valued. In this paper, we consider providers as grouped based on a common characteristic in settings in which certain provider groups have low representation of items in the catalog and, thus, in the user interactions. Then, we envision a scenario wherein platform owners seek to control the degree of exposure to such groups in the recommendation process. To support this scenario, we rely on disparate exposure measures that characterize the gap between the share of recommendations given to groups and the target level of exposure pursued by the platform owners. We then propose a re-ranking procedure that ensures desired levels of exposure are met. Experiments show that, while supporting certain groups of providers by rendering them with the target exposure, beyond-accuracy objectives experience significant gains with negligible impact in recommendation utility. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3477495.3531760 | SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Keywords | DocType | Citations |
Recommender Systems, Collaborative Filtering, Fairness, Ranking | Conference | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mirko Marras | 1 | 3 | 2.74 |
| Ludovico Boratto | 2 | 163 | 30.91 |
| Guilherme Ramos | 3 | 0 | 1.35 |
| Gianni Fenu | 4 | 0 | 0.34 |