  Recommendation systems personalise suggestions to individuals to help them in
their decision making and exploration tasks. In the ideal case, these
recommendations, besides of being accurate, should also be novel and
explainable. However, up to now most platforms fail to provide both, novel
recommendations that advance users' exploration along with explanations to make
their reasoning more transparent to them. For instance, a well-known
recommendation algorithm, such as matrix factorisation (MF), optimises only the
accuracy criterion, while disregarding other quality criteria such as the
explainability or the novelty, of recommended items. In this paper, to the best
of our knowledge, we propose a new model, denoted as NEMF, that allows to
trade-off the MF performance with respect to the criteria of novelty and
explainability, while only minimally compromising on accuracy. In addition, we
recommend a new explainability metric based on nDCG, which distinguishes a more
explainable item from a less explainable item. An initial user study indicates
how users perceive the different attributes of these "user" style explanations
and our extensive experimental results demonstrate that we attain high accuracy
by recommending also novel and explainable items.
