Balancing Between Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders. In Interactive Recommender Systems (IRS), user preferences and the system’s fairness status are constantly changing over time. Existing fairness-aware recommender...

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Detalles Bibliográficos
Autores principales: Liu, Weiwen, Liu, Feng, Tang, Ruiming, Liao, Ben, Chen, Guangyong, Heng, Pheng Ann
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206277/
http://dx.doi.org/10.1007/978-3-030-47426-3_13

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206277/
http://dx.doi.org/10.1007/978-3-030-47426-3_13

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