  Retweet prediction is a challenging problem in social media sites (SMS). In
this paper, we study the problem of image retweet prediction in social media,
which predicts the image sharing behavior that the user reposts the image
tweets from their followees. Unlike previous studies, we learn user preference
ranking model from their past retweeted image tweets in SMS. We first propose
heterogeneous image retweet modeling network (IRM) that exploits users' past
retweeted image tweets with associated contexts, their following relations in
SMS and preference of their followees. We then develop a novel attentional
multi-faceted ranking network learning framework with textually guided
multi-modal neural networks for the proposed heterogenous IRM network to learn
the joint image tweet representations and user preference representations for
prediction task. The extensive experiments on a large-scale dataset from
Twitter site shows that our method achieves better performance than other
state-of-the-art solutions to the problem.
