  Recommendation system could help the companies to persuade users to visit or
consume at a particular place, which was based on many traditional methods such
as the set of collaborative filtering algorithms. Most research discusses the
model design or feature engineering methods to minimize the root mean square
error (RMSE) of rating prediction, but lacks exploring the ways to generate the
reasons for recommendations. This paper proposed an integrated neural network
based model which integrates rating scores prediction and explainable words
generation. Based on the experimental results, this model presented lower RMSE
compared with traditional methods, and generate the explanation of
recommendation to convince customers to visit the recommended place.
