  Comments on social media are very diverse, in terms of content, style and
vocabulary, which make generating comments much more challenging than other
existing natural language generation~(NLG) tasks. Besides, since different user
has different expression habits, it is necessary to take the user's profile
into consideration when generating comments. In this paper, we introduce the
task of automatic generation of personalized comment~(AGPC) for social media.
Based on tens of thousands of users' real comments and corresponding user
profiles on weibo, we propose Personalized Comment Generation Network~(PCGN)
for AGPC. The model utilizes user feature embedding with a gated memory and
attends to user description to model personality of users. In addition,
external user representation is taken into consideration during the decoding to
enhance the comments generation. Experimental results show that our model can
generate natural, human-like and personalized comments.
