The document details the South Tyrol Suggests app, which provides intelligent recommendations for points of interest (POIs) in South Tyrol based on user context. The app features include eco-friendly routing, user preference elicitation, and utilizes a matrix factorization algorithm for rating predictions tailored to contextual conditions. User studies indicate that the app enhances perceived recommendation quality and user satisfaction, with plans for future feature enhancements such as multimodal routing and integration with wearable devices.

1. South Tyrol Suggests - STS
Matthias Braunhofer and Francesco Ricci
Free University of Bozen - Bolzano
Faculty of Computer Science
{mbraunhofer,fricci}@unibz.it

2. STS (South Tyrol Suggests)
• Our Android app on Google Play that supports the
following functionalities:
• Intelligent recommendations for POIs in South Tyrol that are
adapted to the current contextual situation of the user (e.g.,
weather, location, parking status)
• Eco-friendly routing to selected POIs by public or private
transportation means
• Search for various types of POIs across different data sources
(i.e., LTS, Municipality of Bolzano)
• User personality questionnaire for preference elicitation support
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3. Intelligent Recommendations!?!
3
Context Recommendations
Sunny +
Summer
Sunny +
Winter
Rainy

4. Intelligent Recommendations!?!
3
Context Recommendations
Sunny +
Summer
Sunny +
Winter
Rainy

5. Intelligent Recommendations!?!
3
Context Recommendations
Sunny +
Summer
Sunny +
Winter
Rainy

6. Intelligent Recommendations!?!
3
Context Recommendations
Sunny +
Summer
Sunny +
Winter
Rainy

7. Intelligent Recommendations!?!
3
Context Recommendations
Sunny +
Summer
Sunny +
Winter
Rainy

8. Statistics
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• App usually shown in
the top-10 search
results
• Total installs: 891
• Avg. rating/total #:
4.60 / 15

9. Statistics
4
• App usually shown in
the top-10 search
results
• Total installs: 891
• Avg. rating/total #:
4.60 / 15

10. Software Architecture & Implementation
5
Android Client
Presentation
Layer
Apache Tomcat Server
Objects Managed by Spring IoC Container
Spring Dispatcher
Servlet
Spring Controllers
JSON
HTTP
Update Handling
Session Handling
JPA Entities
Hibernate
Service /
Application Layer
Database
Web Services

11. Interaction with the System
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12. Interaction with the System
6

13. Interaction with the System
6

14. Interaction with the System
6

15. Interaction with the System
6

16. Interaction with the System
6

17. Interaction with the System
6

18. Interaction with the System
6

19. Interaction with the System
6

20. Interaction with the System
6

21. Interaction with the System
6

22. Recommendation Task
• Core computations of recommender systems:
• Collection of user preferences (ratings): collect user feedback
(ratings) on items to learn the user preferences
• Rating prediction: a model must be built to predict ratings for
items not currently rated by the user
• Item selection: a model must be built that selects the N most
relevant items for the user
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23. Rating Prediction Algorithm (1/2)
• Rating prediction algorithm is based on Matrix Factorization (MF)
• Basic idea of MF: predict unknown ratings by discovering some
latent features that determine how a user rates an item; features
associated with the user should match with the features
associated with the item
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r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
x
y
z=
r q p
5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qi
Tpu
Rating
prediction
User preference factor
vector
Item preference factor
vector

24. Rating Prediction Algorithm (2/2)
• Context-Aware Matrix Factorization (CAMF): extends standard
MF by incorporating baseline parameters for each contextual
condition and item pair to capture the deviation of the rating for an
item produced by the contextual conditions
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Item average
User
bias
Context
bias
Preference factor (user-
demographics-item-interaction)
Rating = 4
ˆruic1,...,ck
= qi
T
(pu + ya )
a∈A(u)
∑ + i + bu + bicj
j=1
k
∑
cj contextual condition j
qi latent factor vector of item i
pu latent factor vector of user u
A(u) set of user attributes
ya latent factor vector of user attribute a
ī average rating of item i
bu baseline for user u
bicj baseline for item-contextual condition icj
Captures the rating
deviation due to context
(e.g., weather, parking)

25. Evaluation
• Several user studies involving > 100 test users
• Test users were students, colleagues, or other people recruited at the
Klimamobility Fair and Innovation Festival
• Obtained results:
• Recommendation model successfully exploits the weather conditions at
POIs and leads to a higher user’s perceived recommendation quality and
choice satisfaction
• Implemented active learning strategy increases the number of acquired
ratings and recommendation accuracy
• Users largely accept to follow the supported human-computer interaction
and find the user interface clear, user-friendly and easy to use
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26. A/B Testing
• Purpose: reliably determine which system version (A or B) is more
successful
• Prerequisite: you have a system up and running
• Some users see version A, which might be the currently used version
• Other users see version B, which is new and improved in some way
• Evaluate with “automatic” measures (time spent on screens, clicks on a
button, etc.) or surveys (SUS, CSUQ, etc.)
• Allows to see if the new version (B) does outperform the existing version (A)
• Probably the most reliable evaluation methodology
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27. Planned Features
• Integration of a multimodal routing system
• Usage of Facebook profile
• Allow users to plan future visits to POIs
• Provide users with push recommendations
• Exploit activity and emotion information inferred from
wearable devices in the recommendation process
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28. Questions?
Thank you.