Providing personalized offers, and services in general, for the users of a system requires perceiving the context in which the users' preferences are rooted. In this work, we introduce the use of an already known model and methodology-based on the so-called Context Dimension Tree-along with a conceptual architecture to build a recommender system that offers personalized services for travelers. The research is performed in the frame of the Shift2Rail initiative as part of the Innovation Programme 4 of EU Horizon 2020.

1. Context Awareness in
the Travel Companion
of the Shift2Rail
Initiative
Alireza Javadian Sabet, Matteo Rossi,
Fabio A. Schreiber and Letizia Tanca
28th SYMPOSIUM ON ADVANCED DATABASE SYSTEMS
June 21-24, 2020 - Villasimius, Sardinia, Italy

2. Big picture
• Development of an innovative
framework for intelligent
mobility.
• Facilitating the efficient
combination of ride-sharing
and scheduled transport
services.
• Enhancing the performance of
the overall mobility system.

3. Focus of this work
• Build a recommender system
that offers personalized
services to travelers.
Photo by Andrea Bertozzini on Unsplash

4. Solution
Photo by Anne Nygård on Unsplash
• Capturing the individuals’
preferences through
understanding their
CONTEXT

5. Preferences via Context Dimension Tree
• What is the context?
o Context is any information that can be used to characterize the situation of
an entity, where an entity is a person, a place, or an object that is considered
as relevant for the interaction between a user and an application [Dey 2001].
o Context is not just a profile, but an active process of how humans weave
their experience within their whole environment to give it meaning.
• What is the Context Dimension Tree (CDT)?
o A methodology to capture and exploit the information of different situations in
which the user can act, and formalize them hierarchically as a rooted labeled
tree.
o A flexible model to capture the context both in the conceptual and detail level.
[Dey 2001] A. Dey. Understanding and using context. Personal Ubiquitous Comput., 5(1):4-7, 2001

6. Traveler Context Dimension Tree

7. TCDT: Personal Data
• Socio-economic factors
• Useful for user profiling
• Stable preferences
• Enables Warm Start
• Capturing Communities

8. TCDT: Health Issues
• Support Person with
Reduced Mobility (PRM)
• Capture important
preferences that result in
constraints
• Required for logistic
purposes

9. TCDT: Behavioral Status
• Momentary behaviors
o Implicit
o Explicit

10. TCDT: Mobility Request

11. Conceptual System Architecture
Note: It provides an overall view, hiding the details of all the TC's blocks and functions; also, it does not specify
the modules' physical locations (cloud, etc.)

12. Conceptual System Architecture: Knowledge Models
• Knowledge Models:
o Warms start
o Behavioral drifts

13. Conceptual System Architecture: Social Media Core
• Social Media Core:
o Harvest knowledge
from SM platforms
o Publish tailored info
o Share experience

14. Conceptual System Architecture: Recommender Core
• Recommender Core:
o Learning
o Filtering
o Ranking

15. Conceptual System Architecture: Interoperability Framework
• Interoperability Framework:
o Exchange of information
between TC and
different modules

16. Conceptual System Architecture: Trip Tracker
• Trip Tracker:
o Notifications

17. Conceptual System Architecture: BA Dashboard
• Business Analytics Dashboard:
o System performance
o Trends
o Behavioral drifts

18. Challenges and Future works
• Dealing with the change of personal preferences and adopting
the system accordingly.
• Adopting appropriate semantic technologies.
• Dealing with privacy issues.
• Dealing with the heterogeneity of the services provided by
Travel Service Providers.

19. References
• Javadian Sabet A., Rossi M., Schreiber F.A., Tanca L. (2021).
“Towards Learning Travelers’ Preferences in a Context-Aware
Fashion”. In: Ambient Intelligence – Software and Applications.
ISAmI 2020. Advances in Intelligent Systems and Computing,
vol 1239. Springer, Cham. https://doi.org/10.1007/978-3-030-
58356-9_20
• Javadian Sabet A., Rossi M., Schreiber F.A., Tanca L. (2020).
“Context Awareness in the Travel Companion of the Shift2Rail
Initiative”. In: Italian Symposium on Advanced Database
Systems (pp. 199-206). http://ceur-ws.org/Vol-2646/15-
paper.pdf

20. THANK YOU!
QUESTIONS?
This work was supported by Shift2Rail and the EU Horizon 2020 research and
Innovation Programme 4 under grant agreement No: 881825 (RIDE2RAIL)
Alireza.Javadian@polimi.it Matteo.Rossi@polimi.it
Fabio.Schreiber@polimi.it Letizia.Tanca@polimi.it
@ArjSabet
@Ride2Rail
@polimi