  An important task for a recommender system to provide interpretable
explanations for the user. This is important for the credibility of the system.
Current interpretable recommender systems tend to focus on certain features
known to be important to the user and offer their explanations in a structured
form. It is well known that user generated reviews and feedback from reviewers
have strong leverage over the users' decisions. On the other hand, recent text
generation works have been shown to generate text of similar quality to human
written text, and we aim to show that generated text can be successfully used
to explain recommendations.
  In this paper, we propose a framework consisting of popular review-oriented
generation models aiming to create personalised explanations for
recommendations. The interpretations are generated at both character and word
levels. We build a dataset containing reviewers' feedback from the Amazon books
review dataset. Our cross-domain experiments are designed to bridge from
natural language processing to the recommender system domain. Besides language
model evaluation methods, we employ DeepCoNN, a novel review-oriented
recommender system using a deep neural network, to evaluate the recommendation
performance of generated reviews by root mean square error (RMSE). We
demonstrate that the synthetic personalised reviews have better recommendation
performance than human written reviews. To our knowledge, this presents the
first machine-generated natural language explanations for rating prediction.
