Weather plays an important role in tourists’ decision-making and, for instance, some places or activities must not be even suggested under dangerous weather conditions. In this paper we present a context-aware recommender system, named STS, that computes recommendations suited for the weather conditions at the recommended places of interest (POI) by exploiting a novel model-based context-aware recommendation technique. In a live user study we have compared the performance of the system with a variant that does not exploit weather data when generating recommendations. The results of our experiment have shown that the proposed approach obtains a higher perceived recommendation quality and choice satisfaction.

1. Context-Aware Points of Interest
Suggestion with Dynamic Weather
Data Management
Matthias Braunhofer, Mehdi Elahi, Francesco Ricci,
and Thomas Schievenin
Faculty of Computer Science
Free University of Bozen – Bolzano, Italy
ENTER 2014 Research Track
Slide Number 1

2. Agenda
• Recommender Systems and Context-Awareness
• Related Work
• Weather-Aware Recommendations
• Experimental Evaluation
• Conclusions and Future Work
ENTER 2014 Research Track
Slide Number 2

3. Recommender Systems (RSs)
• Goal: recommend new, relevant items to users based on
their feedback on a sample of items (training set)
– Explicit feedback (ratings) vs. implicit feedback (purchase /
browsing history)
• Two basic technical approaches:
– Collaborative filtering (CF)
– Content-based
ENTER 2014 Research Track
Slide Number 3

4. Context is Essential
• Main idea: users can experience items differently
depending on the current contextual situation (e.g.,
season, weather, temperature, mood)
• Example:
ENTER 2014 Research Track
Slide Number 4

5. Context-Aware
Recommender Systems (CARSs)
• CARSs extend RSs beyond users and items to the contexts
in which items are experienced by users
– Rating prediction function is: R: Users x Items x Context 
Ratings
1
3
2
3
4
5
3
1
5
2
5
2
4
5
?
5
4
?
4
2
3
3
4
3
3
ENTER 2014 Research Track
5
5
3
3
5
Slide Number 5

6. Using Weather in RSs
• Hypothesis: weather conditions at places of interest
(POIs), together with past ratings for POIs under several
distinct weather conditions, can be used to improve the
choice satisfaction and perceived recommendation quality
• Example:
?
5
1
?
ENTER 2014 Research Track
Slide Number 6

7. Agenda
ENTER 2014 Research Track
Slide Number 7

8. Context-Aware
Matrix Factorization (CAMF)
• CAMF (Baltrunas et al., 2011) extends matrix factorization
by incorporating baseline parameters for contextual
conditions-item pairs, which capture the deviation of
items‘ ratings due to context
k
ˆ
ruic1 ,...,ck = i + bu + ∑ bic j + pu qiT
j=1
• Main limitations:
ī: average rating of item i
bu: baseline for user u
bicj: baseline for contextual
condition cj and item i
pu: latent factor vector of user u
qi: latent factor vector of item i
– Fails to produce personalized recommendations for new users
– Uses the weather condition around the user rather than the
weather conditions at POIs
ENTER 2014 Research Track
Slide Number 9

9. Mobile CARSs (1/2)
• liveCities (Martin et al., 2011): supports tourists by
sending them push-based notifications when they enter a
certain area and their context matches pre-defined
conditions
• Main limitations:
-
Considers only the
temperature and not the
weather
Uses pre-defined
recommendations rather than
a predictive model
Is only a research prototype
ENTER 2014 Research Track
Slide Number 10

10. Mobile CARSs (2/2)
• VISIT (Meehan et al., 2013): hybrid mobile RS that uses
several contextual factors (i.e., location, time, weather,
social media sentiment) to support tourist‘s decisionmaking process
• Main limitation:
-
System is only a proposal and
not yet implemented
ENTER 2014 Research Track
Slide Number 11

11. Agenda
ENTER 2014 Research Track
Slide Number 12

12. STS – South Tyrol Suggests
ENTER 2014 Research Track
Slide Number 13

13. Weather-Aware
Recommendations
• Main idea: use CAMF (Baltrunas et al., 2011) as starting
point and incorporate additional user attributes (i.e.,
gender, birth date and personality trait information)
• Advantage: can produce personalized recommendations
for users without ratings
CAMF
k
ˆ
ruic1 ,...,ck = i + bu + ∑ bic j + qiT ( pu +
j=1
∑
ya )
a∈A(u )
A distinct factor vector ya corresponds to each
user attribute to describe a user through the
set of user-associated attributes
ENTER 2014 Research Track
Slide Number 14

14. Phase 1: Training
• The system learns the parameters offline (i.e., once every
5 minutes) by minimizing the regularized squared error on
the set of known ratings K (the training set):
Prediction error
minb*,q*, p*,y*
∑
2
(u,i,c1 ,...,ck )∈K
k
ˆ
(ruic1 ,...,ck − ruic1 ,...,ck ) +
λ (b + ∑ b + qi + pu +
2
u
j=1
2
ic j
2
2
∑
2
ya )
a∈A(u )
Regularization term to avoid overfitting
• Minimization performed by stochastic gradient descent
ENTER 2014 Research Track
Slide Number 15

15. Phase 2: Recommendation
• Retrieve the weather and temperature values for the 116
municipalities of South Tyrol by querying Mondometeo
– Note: This information can be cached
• For each POI in the database:
– Look up the POI‘s location
– Assign the weather and temperature values retrieved for the
closest municipality
– Compute a rating prediction, considering the weather and
temperature conditions along with other known contextual
conditions
• Recommend the top-20 POIs to the user
ENTER 2014 Research Track
Slide Number 16

16. Agenda
ENTER 2014 Research Track
Slide Number 17

17. Experimental Methodology
• Live user study where our proposed weather-aware
system (STS) was compared with a variant (STS-S) that has
the same graphical UI but does not use the weather
context when generating recommendations
• We have designed a specific user task and used a
questionnaire for assessing the perceived
recommendation quality and choice satisfaction
(Knijnenburg et al., 2012)
• 54 subjects that were randomly divided in two equal
groups assigned to STS and STS-S (27 each)
ENTER 2014 Research Track
Slide Number 18

18. User Task (1/2)
• Users were supposed to:
– have an afternoon off and to look for attractions / events in South
Tyrol
– consider the contextual conditions relevant for them and to
specify them in the system settings
– browse the attractions / events sections and check whether they
could find something interesting for them
– browse the system suggestions (recommendations), and select
and bookmark the one that they believe fits their needs and
wants
– fill up a survey (Knijnenburg et al., 2012), which measures
perceived recommendation quality and choice satisfaction
ENTER 2014 Research Track
Slide Number 19

19. User Task (2/2)
• After this initial interaction, users had the opportunity to
double check the weather conditions at the bookmarked
POI by accessing on a computer the Mondometeo website
– Will this knowledge influence the users choice?
– Hypothesis: STS has exploited this information so the user should
have already chosen items that are "compatible" with the
weather
• Users were then asked whether they wanted to change
their preferred POI and bookmark another one, i.e., if
they believed that, because of the weather conditions at
the bookmarked POI, their previous choice was not
anymore considered to be appropriate
ENTER 2014 Research Track
Slide Number 20

20. Results (1/2)
Number of unsatisfied users, i.e., those that changed their bookmarked
POI after having double checked the weather conditions at the POI
ENTER 2014 Research Track
Slide Number 21

21. Results (2/2)
Choice satisfaction
Perceived rec. quality
Statement
STS
avg.
STS-S avg.
pvalue
1. I liked the items suggested by the system
4.0
3.7
0.20
2. The suggested items fitted my preference.
3.4
3.4
0.56
3. The suggested items were well-chosen
4. The suggested items were relevant.
3.5
3.7
3.3
3.2
0.13
0.04
5. The system suggested too many bad items.
2.9
2.7
6. I didn’t like any of the suggested items.
3.8
3.3
0.14
<
0.001
7. The items I selected were “the best among the worst”.
8. I like the item I’ve chosen.
3.1
4.6
2.8
4.3
0.20
0.02
9. I was excited about my chosen item.
4.0
3.7
0.03
10. I enjoyed watching my chosen item.
3.7
3.9
0.79
11. The items I watched were a waste of my time.
3.5
3.5
0.42
12. The chosen item fitted my preference.
3.9
4.0
0.71
ENTER 2014 Research Track
Slide Number 22

22. Agenda
ENTER 2014 Research Track
Slide Number 23

23. Conclusions
• STS = mobile CARS that recommends POIs using a set of
contextual factors that include the current weather
conditions at the recommended POIs
• Novelty is the usage of up-to-date weather data into a
matrix factorization algorithm to generate personalized
context-aware recommendations
• Usage of weather data significantly improves the users‘
perceived recommendation quality and choice satisfaction
ENTER 2014 Research Track
Slide Number 24

24. Future Work
• Extended analysis
– To better understand potential performance
differences among the compared CARSs, which may be
due to different usage of the weather contextual
factors
– To test our proposed weather-aware CARS with a
larger number of users and a larger rating dataset
ENTER 2014 Research Track
Slide Number 25

25. Questions?
Thank you.
ENTER 2014 Research Track
Slide Number 26