This dissertation was written as a part of the MSc in ICT Systems at the International
Hellenic University. Its main goal is the examination of the role of human personality in
Movie Recommender systems. We introduce the concept of combining collaborative
techniques with a personality test
so to provide more personalized movie
recommendations.
Previous research has shown some efforts
to incorporate personality in Recommender
systems, but no actual implementation has been attempted on a software level. Using a
renowned movie dataset and the Big Five Personality test, we developed a system with
Python that managed to improve the normal
Movie Recommendation experience by
3.62%.
The findings show that Personalization improves the user’s experience even though
extra effort might be demanded. With further modifications and testing, we can come to
the new age of recommender systems, where personality of the user is as important as it
is in real life.
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