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.
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.
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
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:
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.