Personalization of user experience through recommendations involves understanding their preferences and the context they are living in. In this work, we present a method to rank travel offers returned in response to a travel request made by a user. To give a sensible answer, we learn users' preferences over time and use them to understand travelers' needs. Our solution is based on a data-mining-based recommender system. We first design a database of historical traveler data and populate it with data generated according to rules mimicking the features of actual user profiles. These rules are then used as ground truth to validate the accuracy of the proposed learning algorithm. After performing data pre-processing, a knowledge base is set up by mining association rules from the database, which will then be used along with the travel request to assign a score to each of the potential travel offers, thus ranking them. To test the proposed methodology, we generate synthesized data according to some distributions. The results of the experiments approve the effectiveness of the proposed ranking mechanisms. Finally, we demonstrate the presentation of the ranked offers to the user via some mock-ups of the intended application.

1. Alireza Javadian Sabet, Sankari Gopalakrishnan,
Matteo Rossi, Fabio A. Schreiber, and Letizia Tanca
2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA 2021)
June 28-30, 2021 (Dalian, China)
Preference Mining in the Travel Domain

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.
https://ride2rail.eu/
@ArjSabet
@Ride2Rail
@polimi

3. Travel Companion (TC)
The Travel Companion is an application that is being developed to
support travelers before, during, and after their trips.
@ArjSabet
@Ride2Rail
@polimi

4. Objectives
Designing and implementing the Recommender Core [1] which
provides a personalized set of travel offers to the Travel Companion
(TC) users, ranked based on their contextual preferences model [2].
Javadian et al. 2021 [1]
[3][4]
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@Ride2Rail
@polimi

5. Proposed Methodology
• Employing similar users
historical data.
• Building a knowledge base.
• Scoring the candidate travel
offers.
• Personalized ranking based
on contextual preferences.
@ArjSabet
@Ride2Rail
@polimi

6. Historical Database Design
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7. Knowledge Base Rules
• Tackling Cold Start problem [5] by Association Rules Mining
o Requires a diverse knowledge base containing multiple contexts [6].
• The rules should address:
o Travelers whose preferences are not known/specified.
o Travelers without a past purchasing history.
• Rules:
o Type1: Profile  Preferences
o Type2: Preference  Offers
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@Ride2Rail
@polimi

8. Knowledge Base Procedure
1. Table joins along with preprocessing to serve as the input for
the algorithm.
• Joining Traveler, Travel Request, Request, User Health, User Partner, Service
Preferences, Luggage Preferences, and Meal Preferences tables  UserProfile
• Joining Traveler, Travel Request, Request, User Health, User Partner, Service
Preferences, Luggage Preferences, Meal Preferences, Travel Offer, and Offer Detail
tables  UserOffer
2. Generate rules using the algorithm.
• A-priori algorithm
3. Visualizing and validating the results.
@ArjSabet
@Ride2Rail
@polimi

9. Ranking
1. Compare preferences in travel requests and in the rule
antecedents to get the relevant rules.
2. Use the rule consequents (facilities/features) to rank the
available travel list by how precise the facilities match.
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@Ride2Rail
@polimi

10. Ranking (Preference Vector Comparison)
• Presence of complete vector
• Partial Presence
For example, (pref_healthid=“visual”, pref_mealid=“vegetarian”, pref_luggageid=“bag”)
Complete Match:
(pref_healthid=“visual”, pref_mealid=“vegetarian”)  (offer_serviceid=“airplane”)
Partial Match (with match level 1):
(pref_accid=“pet”, pref_mealid=“vegetarian”)  (offer_facility=“walking”)
Conditions for set comparison:
o Perfect match
o Complete presence
o Partial match
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@Ride2Rail
@polimi

11. Ranking (Scoring)
For each offer:
1. Each rule consequent adds a point to matching offer
multiplied by Confidence.
• item score = relevance * conf; lift > 1
2. Preference vector adds a point to matching offer multiplied by
the priority.
• preference score = relevance * user priority
3. Calculate overall score to rank
• final score = rule score + preference score
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@Ride2Rail
@polimi

12. Validation
• Synthesize data using some pre-defined rules and distributions
to validate the results.
o 1,000 User Profiles
o 3,000 Travel Offers
• Association rules mining
o 4,000+ rules from UserProfile
o 600+ rules from UserOffer
• Ranking
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@Ride2Rail
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13. Top 10 Rules Graph Visualization
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14. Ranking Result
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15. Prototype
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16. Conclusion and Future Work
• Designing and implementing a recommender system to rank
travel offers according to the travelers’ contextual preferences.
• Synthesizing data to test the proposed system.
• Demonstrating the user experience through prototype.
• Future Work:
• Designing personal preference models using supervised learning
algorithms.
• Testing the proposed methodology on EU cities pilots:
• Padua (Italy), Brno (Czech Republic), Athens (Greece), Helsinki (Finland)
https://ride2rail.eu/pilots
@ArjSabet
@Ride2Rail
@polimi

17. References
[1] 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
[2] 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
[3] Hosseini M., Kalwar S., Rossi M., Sadeghi M. (2019). “Automated mapping for semantic-based conversion of
transportation data formats”. In: 1st International Workshop On Semantics For Transport, Vol. 2447, pp. 1-6.
http://ceur-ws.org/Vol-2447/paper7.pdf
[4] Kalwar S., Sadeghi M., Javadian Sabet A., Nemirovskiy A., Rossi M. (2021). “SMART: Towards Automated Mapping
between Data Specifications”. In: The 33rd International Conference on Software Engineering and Knowledge
Engineering, pp. 429-436. https://doi.org/10.18293/SEKE2021-161
[5] Schein, Andrew I., Alexandrin Popescul, Lyle H. Ungar, and David M. Pennock. (2002) “Methods and metrics for
cold-start recommendations." In: Proceedings of the 25th annual international ACM SIGIR conference on Research
and development in information retrieval, pp. 253-260. https://doi.org/10.1145/564376.564421
[6] Ai, Dongmei, Hongfei Pan, Xiaoxin Li, Yingxin Gao, and Di He. (2018). "Association rule mining algorithms on high-
dimensional datasets." In: Artificial Life and Robotics 23, no. 3: 420-427. https://doi.org/10.1007/s10015-018-0437-y
@ArjSabet
@Ride2Rail
@polimi

18. THANK YOU!
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 Sankari.Gopalakrishnan@mail.polimi.it Matteo.Rossi@polimi.it
Fabio.Schreiber@polimi.it Letizia.Tanca@polimi.it
https://ride2rail.eu/
@ArjSabet
@Ride2Rail
@polimi