: Recommender Systems are software used to suggest user items in a personalized and automated way. When combined with Points of Interest (POI), they can set locations as referable items. This type of approach is useful when the amount of POI available for the user is large. In the context of Universities, students have different needs and have to look for different locations to experience the Universities’ resources. The goal of this paper is to present a POI-based Recommender System to improve student’s well-being and to support their academic journey in a Smart Campus. The recommender system was implemented by an application called AONDE, which was used by 110 students, where 63 answered a satisfaction questionnaire allowing the data collection needed for the the system evaluation. An accuracy of 61% in the recommendations of items to students was measured, as well as a high satisfaction rate, where 90.5% of respondents said they were satisfied or very satisfied with the locations suggested by the app. The purpose of this experience paper is demonstrate that the approach here described proved to be useful for students’ routine, impacting positively their academic journey.
[1]
Isabela Gasparini,et al.
A systematic mapping on adaptive recommender approaches for ubiquitous environments
,
2018,
Computing.
[2]
Karin Baier,et al.
Mobile Applications Architecture Design And Development
,
2016
.
[3]
Lior Rokach,et al.
Recommender Systems: Introduction and Challenges
,
2015,
Recommender Systems Handbook.
[4]
Lior Rokach,et al.
Introduction to Recommender Systems Handbook
,
2011,
Recommender Systems Handbook.
[5]
Gerhard Friedrich,et al.
Recommender Systems - An Introduction
,
2010
.
[6]
Roberto Pereira,et al.
Folksonomias: uma análise crítica focada na interação e na natureza da técnica
,
2008,
IHC.