Year: 2024
Type: conference-paper
Abstract: This research explores the challenges of recommendation systems, focusing on diversity and bias mitigation. Excessive precision can create filter bubbles, limiting users’ worldviews and reinforcing existing preferences – a path to injustice and polarization. We investigate how nudge mechanisms can encourage interactions with diverse content, reducing biases. We analyze user interactions with recommendations, both with and without these nudges, using qualitative and quantitative methods. The results show that nudges increase diversity without compromising system quality. This research deepens the understanding of how to integrate diversity into recommendations while maintaining user satisfaction and suggests practical improvements to enhance fairness and improve the user experience.
Authors: Gabrielle Alves, Marcelo G. Manzato
Institution: Universidade de São Paulo
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: gold
OpenAlex ID: W4403389449