  Recommender Systems use the user's profile to generate a recommendation list
with unknown items to a target user. Although the primary goal of traditional
recommendation systems is to deliver the most relevant items, such an effort
unintentionally can cause collateral effects including low diversity and
unbalanced genres or categories, benefiting particular groups of categories.
This paper proposes an approach to create recommendation lists with a
calibrated balance of genres, avoiding disproportion between the user's profile
interests and the recommendation list. The calibrated recommendations consider
concomitantly the relevance and the divergence between the genres distributions
extracted from the user's preference and the recommendation list. The main
claim is that calibration can contribute positively to generate fairer
recommendations. In particular, we propose a new trade-off equation, which
considers the users' bias to provide a recommendation list that seeks for the
users' tendencies. Moreover, we propose a conceptual framework and a decision
protocol to generate more than one thousand combinations of calibrated systems
in order to find the best combination. We compare our approach against
state-of-the-art approaches using multiple domain datasets, which are analyzed
by rank and calibration metrics. The results indicate that the trade-off, which
considers the users' bias, produces positive effects on the precision and to
the fairness, thus generating recommendation lists that respect the genre
distribution and, through the decision protocol, we also found the best system
for each dataset.
