  Explaining automatically generated recommendations allows users to make more
informed and accurate decisions about which results to utilize, and therefore
improves their satisfaction. In this work, we develop a multi-task learning
solution for explainable recommendation. Two companion learning tasks of user
preference modeling for recommendation} and \textit{opinionated content
modeling for explanation are integrated via a joint tensor factorization. As a
result, the algorithm predicts not only a user's preference over a list of
items, i.e., recommendation, but also how the user would appreciate a
particular item at the feature level, i.e., opinionated textual explanation.
Extensive experiments on two large collections of Amazon and Yelp reviews
confirmed the effectiveness of our solution in both recommendation and
explanation tasks, compared with several existing recommendation algorithms.
And our extensive user study clearly demonstrates the practical value of the
explainable recommendations generated by our algorithm.
