2018/01/01 by Jurek Leonhardt, Avishek Anand, Megha Khosla
Business, Management and Accounting · Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Aggregate (composite) #Consumer Market Behavior and Pricing #Diversification (marketing strategy) #Diversity (politics) #Recommender Systems and Techniques #Recommender system #Work (physics) #cs.CY #cs.IR
paper · pdf · doi:10.1145/3184558.3186949
openalex publication_date 2018/01/01 · openalex created_date 2018/05/07 · arxiv created 2018/07/17 · arxiv updated 2018/07/18 · openalex updated_date 2026/08/05
Recent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-processing algorithms aimed at only improving diversity among recommendations lead to discrimination among the users. We introduce the notion of user fairness which has been overlooked in literature so far and propose measures to quantify it. Our experiments on two diversification algorithms show that an increase in aggregate diversity results in increased disparity among the users.