Marko Tkalcic, L. Chen,
"Personality and Recommender Systems"
, Serie Recommender Systems Handbook, Springer, 2016
Personality and Recommender Systems
Sprache des Titels:
Personality, as defined in psychology, accounts for the individual differences
in users? preferences and behaviour. It has been found that there are significant
correlations between personality and users? characteristics that are traditionally
used by recommender systems ( e.g. music preferences, social media behaviour,
learning styles etc.). Among the many models of personality, the Five Factor Model
(FFM) appears suitable for usage in recommender systems as it can be quantitatively
measured (i.e. numerical values for each of the factors, namely, openness,
conscientiousness, extraversion, agreeableness and neuroticism). The acquisition of
the personality factors for an observed user can be done explicitly through questionnaires
or implicitly using machine learning techniques with features extracted
from social media streams or mobile phone call logs. There are, although limited,
a number of available datasets to use in offline recommender systems experiment.
Studies have shown that personality was successful at tackling the cold-start problem,
making group recommendations, addressing cross-domain preferences4 and at
generating diverse recommendations. However, a number of challenges still remain.