The new user problem is an important and challenging issue that Context-Aware Recommender Systems (CARSs) must deal with, especially in the early stage of their deployment. It occurs when a new user is added to the system and there is not enough information about the user’s preferences in order to compute appropriate recommendations. It is common to address this problem in the recommendation algorithm, by using demographic attributes such as age, gender, and occupation, which are easy to collect and are reasonably good predictors of the user preferences. However, as we show here, user’s personality provides even better information for generating context-aware recommendations for places of interest (POI), and it is still easy to assess with a simple questionnaire. In our study, using a rating data set collected by a mobile app called STS (South Tyrol Suggests), we have found that by considering the user personality the system can better rank the recommendations for the new users.

1. ENTER 2015 Research Track Slide Number 1
User Personality and the New User
Problem in a Context-Aware POI
Recommender System
Ma$hias Braunhofer, Mehdi Elahi, and Francesco
Ricci
Faculty of Computer Science
Free University of Bozen – Bolzano, Italy

2. ENTER 2015 Research Track Slide Number 2
Agenda
• Context Aware Recommender Systems (CARS)
• New user problem in CARS – cold start
• SoluIon: demographic and personality informaIon usage
• Experimental comparison
• Conclusions and future work

3. ENTER 2015 Research Track Slide Number 3
IntroducGon
• Recommender Systems (RSs) are tools that support users
decision making by suggesting products that can be
interesting to them.
• Examples of Recommender Systems:

4. ENTER 2015 Research Track Slide Number 4
State-of-the-art Technique
• CollaboraGve Filtering:
• Predicts unknown raIngs exploiIng raIngs given by users to
items
• The items with the highest predicted raIngs are recommended
• If a new user has no raIng, it is impossible to compute any raIng
predicIon for her (cold start problem).
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5. ENTER 2015 Research Track Slide Number 5
• Extends collaboraIve filtering by considering the contexts in
which the items are experienced by the users
• The Cold Start problem becomes even worse.
Context-Aware RS (CARS)
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RS CARS

6. ENTER 2015 Research Track Slide Number 6
Related Works
• Common soluIons of the cold-start problem in CARSs:
– (Branhaufer et al. 2014) proposes a hybrid soluIon that exploits a
selecIon of two CARS algorithms, each one suited for a parIcular cold-
start situaIon, and switches between these algorithms depending on
the detected situaIon (new user, new item or new context).
– (Codina et al. 2013) proposes SemanIc Pre-Filtering (SPF) which
exploits, in the recommendaIon process, not only contextual raIngs
that exactly match the target context but also those there were
acquired in semanIcally similar contexts.
– (Zheng, et al. 2013) proposes weighIng contextual raIngs based on
their similarity to a given target context to tackle cold start in CARS.

7. ENTER 2015 Research Track Slide Number 7
Our Approach
• Cold start problem in CARS:
– a new user, without raIng history, requests a recommendaIon to the
system (new user problem)
• Approach:
– The system exploits the user's personality informaIon in the
recommendaIon process to detect hidden factors modelling the
preferences of the user
• Outcome:
– using personality informaIon, our CARS called STS could generate
personalized recommendaIons for the new users, with higher
accuracy compared to a baseline that uses demographic informaIon.

8. ENTER 2015 Research Track Slide Number 8
South Tyrol Suggests (STS)
• A mobile Android context-aware
RS that recommends places of
interests (POIs) in the South Tyrol
region, Italy.
• The system was in an extreme
cold-start situation (only 700
ratings for total of 27,000 POIs).

9. ENTER 2015 Research Track Slide Number 9
STS: Demographic Info
• In Sign-up process, STS asks to the
users her demographic data, i.e.,
her gender and birthday.

10. ENTER 2015 Research Track Slide Number 10
STS: Personality Info
NeuroGcism ConscienGous-
ness
Openness
Extraversion Agreeableness
Big Five
Personality Traits

11. ENTER 2015 Research Track Slide Number 11
STS: Personality Info
NeuroGcism ConscienGous-
ness
Openness
Extraversion Agreeableness
Big Five
Personality Traits

12. ENTER 2015 Research Track Slide Number 12
STS: Contextual Factors

13. ENTER 2015 Research Track Slide Number 13
STS: RaGng ElicitaGon
• By knowing the user's personality the
system idenIfies and presents POIs that she
has experienced, and can rate.
• For each POI, STS presents 3 contextual
factors, that user can specify freely, if she
remembers.

14. ENTER 2015 Research Track Slide Number 14
STS: RecommendaGons
• STS computes raGng predicGons for all
POIs, using the users' personality and the
raIngs they have given to POIs
• The top POIs with the highest predicted
raIngs are recommended to the user.

15. ENTER 2015 Research Track Slide Number 15
RecommendaGon Algorithm
• Context-aware Matrix FactorizaGon (CAMF) (Baltrunas et al.,
2011) extends matrix factorizaIon by incorporaIng baseline
parameters: they model overall deviaIons of items‘ raIngs
produced by the nature of the users, items, and contexts
ˆruic1,...,ck
= i + bu + bicj
j=1
k
∑ + puqi
T
ī: average raIng of item i
bu: baseline for user u
bicj
: baseline for contextual
condiGon cj and item i
pu: latent factor vector of user u
qi: latent factor vector of item i

16. ENTER 2015 Research Track Slide Number 16
EvaluaGon: Goals
• We are interested in:
– comparing the accuracy of recommendaIons (based on
CAMF model) for new users, by using either:
• demographics informaIon
• Or the Big-5 personality traits
– IdenIfying the demographic a$ributes or Big-5 personality
traits that generate more accurate recommendaIons for
new users.

17. ENTER 2015 Research Track Slide Number 17
EvaluaGon: Dataset
Total number of ratings 1,379
Number of users 239
Number of items 184
Number of contextual factors 14
Number of contextual conditions 56
Number of contextual situations 799
Number of demographic attributes 2
Number of personality attributes 5
New version of dataset is available for download (ResearchGate login required):
h$ps://www.researchgate.net/publicaIon/305682479_Context-Aware_Dataset_STS_-_South_Tyrol_Suggests_Mobile_App_Data

18. ENTER 2015 Research Track Slide Number 18
EvaluaGon: Dataset
Example of
contextually
tagged raIng
With friends crowded
a_ernoon

19. ENTER 2015 Research Track Slide Number 19
EvaluaGon Methodology
• Methodology: ten-fold cross-validaIon scheme (Braunhofer et al.,
2014, Shani et al., 2008):
– First, we randomly split the users in the data set into 10
subsets
– Then, for 10 runs, we use the raIngs of one subset of users as
tesIng set and the remaining ones as training set
– In this way we create a test set of raIngs coming from users
that have no raIngs in the training set, i.e., really cold (new)
users
– The predicIon model is trained on the training set and tested
on the test set (raIng predicIons are compared to stored
raIngs).

20. ENTER 2015 Research Track Slide Number 20
EvaluaGon Metrics
• Mean Absolute Error
– The lower the be$er
– Measures the average absolute deviaIon of the predicted raIng from
the user's true raIng:
• Normalized Discounted CumulaGve Gain:
– The higher the be$er
– The recommendaIons for u are sorted according to the predicted raIng
values, then DCGu is computed:

21. ENTER 2015 Research Track Slide Number 21
Results
Comparison of MAE (the lower the be`er) and
nDCG (the higher the be`er)

22. ENTER 2015 Research Track Slide Number 22
Conclusions
• We have shown that:
– Personality informaIon gives a be$er raIng predicIon
model than demographic informaIon - which is a more
common approach to tackle the cold-start problem
– Using even a single personality trait (out of five) can
sIll produce a significant improvement of the
recommendaIon quality.

23. ENTER 2015 Research Track Slide Number 23
Future Work: Datasets
• Such as MyPersonality:
• Facebook app for taking psychometric tests
• more than 6,000,000 personality profiles
• more than 4,000,000 of Facebook profiles

24. ENTER 2015 Research Track Slide Number 24
Future Work: Social Mining
• The personality of the users can be learnt from their
interacIons in social networks (Bachrach et al., 2012)
• Social profiles can even be used directly in
collaboraIve filtering based RSs (Fernández-Tobías et
al., 2014).

25. ENTER 2015 Research Track Slide Number 25
Open Issues: GamificaIon
• Can we make the personality acquisiIon more fun?
Shoot the ball to the
personality trait that is
strongest in you
NeuroGcism ConscienGo
us-ness
Openness
Extraversio
n
Agreeablen
ess Big Five
Personality
Traits

26. ENTER 2015 Research Track Slide Number 26
Thank you!