Enhancing Long Term Fairness in Recommendations with Variational Autoencoders
Rodrigo Borges, Kostas Stefanidis · 2019
Recommender systems have become indispensable for several Web sites, helping users deal with big amounts of data. They are capable of analyzing user/item interactions taking place on-line, and provide each user with a list of suggestions sorted by relevance. Items with the same or very close relevance, however, may occupy different positions in the ranking and may be exposed to completely different levels of attention. This promotes unfair treatment and can only be addressed by a long term strategy.