Personal mobile devices and powerful data filtering algorithms enhance the context-awareness of personalised recommendation systems of tourism resources. These devices supply with computing capabilities, on board sensors, ubiquitous Internet access and continuous user monitoring, whereas the filtering algorithms allow matching tourists' profiles (context and interests) to a large knowledge base of tourism resources. Furthermore, personal mobile devices can gather user-related information, including the user context and access to multiple data sources. However, the creation and maintenance of such a knowledge base (tourism-related resources) requires a collaborative approach due to the heterogeneity, volume and dynamic nature of these resources. The current work aims to solve the problem by adopting a Crowdsourcing approach for the collaborative maintenance of the resources' knowledge base, Trust and Reputation to validate uploaded resources as well as publishers, Big Data for user profiling and context-aware filtering algorithms for the personalised recommendation of tourism resources.
Fátima Leal holds an MSc in Electrical and Computers Engineering (Major in Telecommunciations) and a BSc. in Electrical and Computers Engineering, both from the Instituto Superior de Engenharia do Porto, Portugal. She is currently enrolled in the "Information and Communication Technologies" PhD program of the University of Vigo. She is also, a collaborator at the INESC research center in Porto. Her research is focused on tourism-related mobile technologies based on Crowdsourcing with Trust and Reputation algorithms, Big Data and Context-aware Recommendation Systems.