  Recommender systems are intrinsically tied to a reliability/coverage dilemma:
The more reliable we desire the forecasts, the more conservative the decision
will be and thus, the fewer items will be recommended. This causes a detriment
to the predictive capability of the system, as it is only able to estimate
potential interest in items for which there is a consensus in their evaluation,
rather than being able to estimate potential interest in any item. In this
paper, we propose the inclusion of a new term in the learning process of matrix
factorization-based recommender systems, called recklessness, that takes into
account the variance of the output probability distribution of the predicted
ratings. In this way, gauging this recklessness measure we can force more spiky
output distribution, enabling the control of the risk level desired when making
decisions about the reliability of a prediction. Experimental results
demonstrate that recklessness not only allows for risk regulation but also
improves the quantity and quality of predictions provided by the recommender
system.
