Imagine you are in Milano for the Design Week. You have just spent a couple of days attending few nice events in Brera district. Which of the other hundreds of events spread around in Milano shall you attend now? This paper presents a system able to recommend venues to the visitors of such a city-scale event based on the digital footprints they left on Social Media. By combining deductive and inductive stream reasoning techniques with visitor-modeling functionality, this system se- mantically analyses and links visitors' social network activities to pro- duce high-quality recommendations even when information about visitors' preferences for venues and events is sparse.

A Case Study of Active, Continuous and Predictive Social Media Analytics for Smart City

Palpanas, Themistoklis;Tsytsarau, Mikalai
2014-01-01

Abstract

Imagine you are in Milano for the Design Week. You have just spent a couple of days attending few nice events in Brera district. Which of the other hundreds of events spread around in Milano shall you attend now? This paper presents a system able to recommend venues to the visitors of such a city-scale event based on the digital footprints they left on Social Media. By combining deductive and inductive stream reasoning techniques with visitor-modeling functionality, this system se- mantically analyses and links visitors' social network activities to pro- duce high-quality recommendations even when information about visitors' preferences for venues and events is sparse.
2014
Proceedings of the Fifth Workshop on Semantics for Smarter Cities a Workshop at the 13th International Semantic Web Conference
AA. VV.
Aachen
CEUR-WS. org
M., Balduini; S., Bocconi; A., Bozzon; E., Della Valle; Y., Huang; J., Oostermann; Palpanas, Themistoklis; Tsytsarau, Mikalai
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/101716
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