Experimentation with Fairness-Aware Recommendation Using Librec-Auto

Explore fairness-aware recommendation systems in this comprehensive tutorial from FAT*2020. Delve into the intricacies of recommendation algorithms, stakeholder concerns, and quality of service issues. Learn about diversity in recommendations, individual fairness, and system monitoring. Gain hands-on experience with librec-auto, a tool for experimenting with fairness-aware recommendations. Discover methodological approaches and parameter sensitivity in recommendation systems. Engage with experts Robin Burke and Masoud Mansoury as they present their collaborative work on fairness in recommender systems.

Introduction
Agenda
About us
References
Handson
Online Forum
What is recommendation
Differences in recommendation
Stakeholders in recommendation
Provider concerns
Other stakeholders
Quality of service
Diversity literature
Individual fairness
The hard truth
Monitoring the system
Philosophy
librec
methodological detour
parameters sensitivity