This document discusses using cross-domain user preferences and personality traits to improve collaborative filtering recommendations. It proposes using personality-based collaborative filtering heuristics and factor models that incorporate both preferences from other domains and personality data. Experiments on a dataset of Facebook likes and personality tests show these methods can enhance recommendations, especially in cold-start situations, by exploiting relationships between preferences and personality across domains like movies, music and books.

1. On the use of
cross-domain user preferences and
personality traits
in collaborative filtering
Ignacio Fernández-Tobías, Iván Cantador
{ignacio.fernandezt, ivan.cantador}@uam.es
Information Retrieval Group
Universidad Autónoma de Madrid, Spain

2. 1
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
The Five-Factor model of personality
Openness (OPE)
Conscientiousness (COS)
Extraversion (EXT)Agreeableness (AGR)
Neuroticism (NEU)
cautious/consistent vs.
curious/inventive
careless/easy-going vs.
organized/efficient
solitary/reserved vs.
outgoing/energetic
cold/unkind vs.
friendly/compassionate
secure/calm vs.
unconfident/nervous
tendency to intellectual
curiosity, creativity and
preference for novelty and
variety of experiences
tendency to show self-discipline and
aim for personal achievements, and to
have an organized (not spontaneous)
and dependable behavior
tendency to seek stimulation in the
company of others, and to put energy
in finding positive emotions
tendency to be kind,
concerned, truthful and
cooperative towards others
tendency to experience
unpleasant emotions, and
low degree of emotional
stability and impulse control

3. 2
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
• Music
• (Rentfrow & Gosling , 2003): reflective people (high OPE)  jazz, blues, classical music;
energetic people (high EXT and high AGR)  rap, hip-hop, funk, electronic music
• (Rawlings & Ciancarelli, 1997): high OPE  high diversity of music preferences;
high EXT  popular music
• Movies and TV shows
• (Chausson, 2010): high OPE  comedy & fantasy movies; high COS  action movies;
high NEU  romantic movies
• (Odić et al., 2013): emotional patterns induced by movies as functions of EXT, AGR, NEU
• Multiple domains (music, movies-TV shows, books-magazines, ...)
• (Rentfrow et al., 2011): relations between preferences and personality-based
categories, e.g. aesthetic, cerebral, communal, dark, and thrilling
• (Kosinski et al., 2012): relations between preferences and personality for certain
websites and website categories
• (Cantador et al., 2013): association rules between preferences and personality factors
User preferences and personality traits

4. 3
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
Relations between user preferences and
personality traits may be exploited in
personalization and recommendation services

5. 4
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
• Addressing cold-start situations
• (Hu & Pu, 2010; Tkalčič et al., 2011): user similarities in CF with personality information
• Mitigating the sparsity problem
• (Hu & Pu, 2011): increasing the density of rating matrices by means of personality data
• Facilitating the user preference elicitation
• (Elahi et al., 2013): exploiting the user’s personality to identify the items to rate
• Improving recommendation accuracy
• (Nunes et al., 2009): user models composed of personality factors & facets
• (Roshchina , 2012): personality-aware CB and CF recommendation models
• (Fernández-Tobías & Cantador, 2014): incorporating both user preferences and
personality factors into CF heuristics
• (Wu & Chen, 2015): integrating implicitly acquired personality profiles into CF
heuristics
•
Exploiting user personality in recsys

6. 5
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
• Addressing cold-start situations
• (Hu & Pu, 2010; Tkalčič et al., 2011): user similarities in CF with personality information
• Mitigating the sparsity problem
• (Hu & Pu, 2011): increasing the density of rating matrices by means of personality data
• Facilitating the user preference elicitation
• (Elahi et al., 2013): exploiting the user’s personality to identify the items to rate
• Improving recommendation accuracy
• (Nunes et al., 2009): user models composed of personality factors & facets
• (Roshchina , 2012): personality-aware CB and CF recommendation models
• (Fernández-Tobías & Cantador, 2014): incorporating both user preferences and
personality factors into CF heuristics
• (Wu & Chen, 2015): integrating implicitly acquired personality profiles into CF
heuristics
• Enhancing cross-domain recommendations
Exploiting user personality in recsys

7. 6
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
Cross-domain recommenders systems
aim to generate or enhance recommendations in a target
domain by exploiting knowledge from source domains
Cantador, I., Fernández-Tobías, I., Berkovsky, S., Cremonesi, P. 2015. Cross-domain
Recommender Systems. In Ricci, F., Rokach, L., Shapira, B., Kantor, P. B. (Eds.), Recommender
Systems Handbook - 2nd edition. To appear
Target
domain
Source
domain
+
knowledge
aggregation
target domain
recommendations
Target
domain
Source
domain 
knowledge
linkage/transfer
target domain
recommendations
Aggregating knowledge Linking/transferring knowledge

8. 7
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
How user preferences in a source domain and
personality factors can be exploited to provide
effective recommendations in a target domain?

9. 8
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
How user preferences in a source domain and
personality factors can be exploited to provide
effective recommendations in a target domain?
1. Personality-based CF heuristics
2. Personality-based CF factor models

10. 9
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
Proposed personality-based CF heuristics
rating estimation
hybrid user similarity
personality-based
COS
PEA
SPE
preference-based
CF

11. 10
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
• Matrix factorization (MF) recommendation
• Cross-domain MF recommendation
• Personality-based cross-domain MF recommendation
Proposed personality-based CF factor models

12. 11
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
• A dataset obtained from the MyPersonality project*
• Facebook likes for movies, music items, and books
• Five-factor personality profiles from psychometric questionnaires
‐ Revised NEO Personality Inventory (NEO PI-R): 60-240 questions
• Statistics (updated from those reported in the paper)
Experiments - dataset
* http://mypersonality.org. We thank David Stillwell & Michal Kosinski for their support

13. 12
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
• Cross-domain situations
• user overlap
• no user overlap
• Cold-start situations in the target domain
• extreme: 0 training ratings per user
• moderate: from 1 to 10 training ratings per user
• Additional user information
• source-domain ratings
• personality factors
Experiments - evaluation scenarios

14. 13
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
• Baseline recommendation methods
• Random
• Most popular
• Item-based kNN (k=∞) and User-based kNN (k=50, but others tested as well)
• MF (10 factors, but others tested as well)
• Proposed recommendation methods
• Personality-based heuristics (λ=0, 0.1, …, 0.9; note that λ=1 is user-based
kNN)
• Personality-based MF models
• Recommendation performance metrics
• Accuracy and ranking metrics for positive feedback: mainly MAP, but other
metrics computed (Half-life utility, Mean Percentage Ranking)
• Coverage and novelty
• 5-fold cross validation + statistical significance tests
Experiments - evaluation setting

15. 14
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
• In “extreme” cold-start situations
• Recommendation methods
‐ MF is not capable of providing recommendations
‐ Personality-based MF outperforms Popularity
• Cross domains:
‐ Movies  Books
‐ Music + personality  Movies
‐ Movies + personality  Music
• In “moderate” cold-start situations
• Recommendation methods:
‐ Both MF and personality-based MF clearly outperform Popularity
‐ Personality-based MF performs slightly better than MF
• Cross domains:
‐ Music + personality  Movies (small improvements)
‐ Movies + personality  Music (clear improvements)
Experiments - result highlights

16. 15
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
• Both user personality factors and cross-domain preferences
allow dealing with cold-start situations
• best results achieved when crossing the movies and music domains
• experiments conducted with likes instead of numeric ratings
• Reasonable recommendation performance improvements
• moderate better accuracy- and ranking-based performance
• better diversity and coverage performance
• Difficulty to represent user personality in an effective way
(for recommendation purposes)
• discretization of personality factor values
Conclusions

17. 16
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
• Personality factors
• (Hu & Pu, 2010; Tkalčič et al., 2011; Fernández-Tobías & Cantador, 2014): user
profiles composed of numeric values of personality factors
• Personality facets
• (Nunes et al., 2009): e.g. OPE facets: imagination, artistic interests,
emotionality, ...
• Personality categories
• (Rentfrow & Gosling , 2003): e.g. reflective and energetic people (user level)
• (Rentfrow et al., 2011): e.g. aesthetic, cerebral and thrilling contents (item
level)
• Personality user stereotypes
• (Lin & McLeod, 2002): human temperaments from Keirsey’s theory: guardian,
idealist, rational, artisan
Future work - Personality user models

18. 17
On the use of cross-domain user preferences and personality traits in collaborative filtering
23rd Conference on User Modelling, Adaptation and Personalization (UMAP 2013)
3rd July 2015, Dublin, Ireland
Future work - User attributes
• age and gender
• (Fernández-Tobías & Cantador, 2014): different correlations between
preferences and personality profiles depending on the users’ gender and age
- Erikson’s psychosocial stages (1950)
• educational attainment
• e.g., “people with high levels of education may be more open-minded, and
thus have larger and more diverse sets of preferences”
• others…

19. On the use of
cross-domain user preferences and
personality traits
in collaborative filtering
Ignacio Fernández-Tobías, Iván Cantador
{ignacio.fernandezt, ivan.cantador}@uam.es
Information Retrieval Group
Universidad Autónoma de Madrid, Spain