  We tackle the problem of building explainable recommendation systems that are
based on a per-user decision tree, with decision rules that are based on single
attribute values. We build the trees by applying learned regression functions
to obtain the decision rules as well as the values at the leaf nodes. The
regression functions receive as input the embedding of the user's training set,
as well as the embedding of the samples that arrive at the current node. The
embedding and the regressors are learned end-to-end with a loss that encourages
the decision rules to be sparse. By applying our method, we obtain a
collaborative filtering solution that provides a direct explanation to every
rating it provides. With regards to accuracy, it is competitive with other
algorithms. However, as expected, explainability comes at a cost and the
accuracy is typically slightly lower than the state of the art result reported
in the literature.
