  Explaining to users why some items are recommended is critical, as it helps
users to make better decisions, increase their satisfaction, and gain their
trust in recommender systems (RS). However, existing explainable RS usually
consider explanations as side outputs of the recommendation model, which has
two problems: (1) it is difficult to evaluate the produced explanations because
they are usually model-dependent, and (2) as a result, the possible impacts of
those explanations are less investigated.
  To address the evaluation problem, we propose learning to explain for
explainable recommendation. The basic idea is to train a model that selects
explanations from a collection as a ranking-oriented task. A great challenge,
however, is that the sparsity issue in the user-item-explanation data would be
severer than that in traditional user-item relation data, since not every
user-item pair can associate with multiple explanations. To mitigate this
issue, we propose to perform two sets of matrix factorization by considering
the ternary relationship as two groups of binary relationships. To further
investigate the impacts of explanations, we extend the traditional item ranking
of recommendation to an item-explanation joint-ranking formalization. We study
if purposely selecting explanations could achieve certain learning goals, e.g.,
in this paper, improving the recommendation performance. Experiments on three
large datasets verify our solution's effectiveness on both item recommendation
and explanation ranking. In addition, our user-item-explanation datasets open
up new ways of modeling and evaluating recommendation explanations. To
facilitate the development of explainable RS, we will make our datasets and
code publicly available.
