|Abstract||In the present work, algorithms based on complex network theory are applied to Recommendation Systems
in order to improve their quality of predictions. We show how some
networks are grown under the influence of trendy forces, and how this
can be used to enhance the results of a recommendation system, i.e.
increase their percentage of right predictions. After defining a base
algorithm, we create recommendation networks which are based on a
histogram of user ratings, using therefore an underlying principle of preferential attachment.
We show the influence of data aging in the prediction of user habits
and how the exact moment of the prediction influences the
recommendation. Finally, we design weighted networks that take into
account the age of the information used to generate the links. In this
way, we obtain a better approximation to evaluate the users' tastes.