Location recommendations from user mobile trajectories

Abstract

Location Recommenders (LR) are location based recommender systems which use locations from historic user trajectories to recommend new places such as locations of sights, museums, or other places of interest by learning past preferences. In this work we analyze the viability of different LR models to be used with mobile hotspots which are handed out to tourists in different hotels in the city of Vienna. As these devices can be potentially used by thousands of users at any given time, we explore different methods of data collection, management and visualization. Whenever systems are used in assisting humans with their decision processes, the implications of algorithmically made recommendations need to be analyzed. For LR fairness can be defined for both users and locations, as both can be subject to unfairness. We propose a framework in which multiple fairness aspects can be formulated. We compare the LR in three fairness aspects formulated in the framework and their predictive accuracy, to give a final assessment of which LR is preferable in this scenario.7