We talk about Smart Tourism when tourism happens in Smart Cities. Therefore, Smart Tourism grounds on a reliable and fast exchange of information (between sensors) aimed to help tourists in accessing real-time services. For instance, a tourist from her mobile, that interacts in the background with the Smart City sensors, may identify that her bus is delaying or that the place she planned to visit is currently over-crowded. We think that this is not making tourism smart, but only less annoying. In order to improve the whole tourism experience, we have build novel technology based on Free Software by adding to Smart Cities a data-driven intelligence that harnesses the huge amount of data produced by both sensors and tourists. In this talk, we present the two main components of our technology: a data analytics tool, essential for far-sighted decision-makers, and a Recommender System, to be directly used by tourists.

1. Improving the Tourism Experience
via Free Software
David Massimo
Free University of Bolzano
damassimo@unibz.it

5. Motivation
Adapt recommendation to learnt user behavior
Understanding User preferences in Context
Support tourists in finding points of interest (POIs)
Huge variety of different POIs
Harness human-environment interactions
enabled sensors (GPS, IoT)
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SUNNY
AFTERNOON
MUSEUM
Use explicit knowledge about POIs (features)

6. 6
Often there is not enough user
specific behavioural data

7. DOC-Like representation
POI-visit trajectory
Clustering Users’ Visit Trajectories
Extract important keywords and combine them
into a document visit
Cluster visit documents
Each cluster models a group of similar behaviours
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8. 8
PROBLEM SETUP
+50 -10
POLICY
Technical Approach
Reward and Action-selection policy are learnt via
Inverse Reinforcement Learning
Markov Decision Process
From the knowledge of the reward (utility) the user gets by visiting the
POIs, an MDP is solved to compute the policy the user follows to achieve
her goal
? ??
REWARD
D. Massimo, F. Ricci: Harnessing a generalised user behaviour model for next-POI recommendation. RecSys 2018: 402-406

10. Our RS approach identify items that the user will
happily choose 10
D. Massimo, F. Ricci: Users’ Evaluation of Next-POI Recommendations. Rectour@RecSys 2019

11. LOW LEVEL APIs
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VISUALIZATION
seaborn
MACHINE LEARNING & DATA ANALYSIS
Free Software Frameworks
BACKEND &
FRONTEND
HIGH LEVEL APIs

12. Wondervalley is a project of the Faculty of Computer Science and the
Faculty of Economics and Management at the Free University of Bolzano.
Discover Plan
Get personalized tips and discover
hidden gems in South Tyrol!
Evaluate the places you visited.
Plan the activities you would like
to do.
Evaluate
Discover what surrounds you!
http://wondervalley.unibz.it

13. IoT infrastructure
+3400 Beacon devices

14. Travellers mobility
in South Tyrol
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15. 15
Bolzano
LEARNING AT
CITY SCALE
MOBILITY
Access point
Pedestrian vs Vehicle
Local vs Tourist

16. 16MAXMIN
Bolzano
LEARNING AT
CITY SCALE
MOBILITY
Access point
Pedestrian vs Vehicle
Local vs Tourist
BEHAVIOUR
Local vs Tourist
Choices (visit actions)
Preferences

17. Support Free Software Visit us @ faculty of CS!
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