Towards Personality-Aware Recommendation

Giorgio Roffo

Abstract:

in the last decade new ways of shopping online have increased the
possibility of buying products and services more easily and faster
than ever. In this new context, personality is a key determinant
in the decision making of the consumer when shopping. The two
main reasons are: firstly, a person’s buying choices are influenced
by psychological factors like impulsiveness, and secondly, some
consumers may be more susceptible to making impulse purchases
than others. To the best of our knowledge, the impact of person-
ality factors on advertisements has been largely neglected at the
level of recommender systems. This work proposes a highly inno-
vative research which uses a personality perspective to determine
the unique associations among the consumer’s buying tendency and
advert recommendations. As a matter of fact, the lack of a pub-
licly available benchmark for computational advertising do not al-
low both the exploration of this intriguing research direction and
the evaluation of state-of-the-art algorithms. We present the ADS
Dataset, a publicly available benchmark for computational adver-
tising enriched with Big-Five users’ personality factors and 1,200
personal users’ pictures. The proposed benchmark allows two main
tasks: rating prediction over 300 real advertisements (i.e., Rich Me-
dia Ads, Image Ads, Text Ads) and click-through rate prediction.
Moreover, this work carries out experiments, reviews various eval-
uation criteria used in the literature, and provides a library for each
one of them within one integrated toolbox