Defining and measuring fairness in location recommendations

Abstract

Location-based recommender systems learn from historical movement traces of users in order to make recommendations for places to visit, events to attend, itineraries to follow. As with other systems assisting humans in their decisions, there is an increasing need to scrutinize the implications of algorithmically made location recommendations. The challenge is that one can define different fairness concerns, as both users and locations may be subjects of unfair treatment. In this work, we propose a comprehensive framework that allows the expression of various fairness aspects, and quantify the degree to which the system is acting justly. In a case study, we focus on three fairness aspects, and investigate several types of location-based recommenders in terms of their ability to be fair under the studied aspects.