  Textual explanations have proved to help improve user satisfaction on
machine-made recommendations. However, current mainstream solutions loosely
connect the learning of explanation with the learning of recommendation: for
example, they are often separately modeled as rating prediction and content
generation tasks. In this work, we propose to strengthen their connection by
enforcing the idea of sentiment alignment between a recommendation and its
corresponding explanation. At training time, the two learning tasks are joined
by a latent sentiment vector, which is encoded by the recommendation module and
used to make word choices for explanation generation. At both training and
inference time, the explanation module is required to generate explanation text
that matches sentiment predicted by the recommendation module. Extensive
experiments demonstrate our solution outperforms a rich set of baselines in
both recommendation and explanation tasks, especially on the improved quality
of its generated explanations. More importantly, our user studies confirm our
generated explanations help users better recognize the differences between
recommended items and understand why an item is recommended.
