Abstract
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 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.
Keywords
- Machine Learning: Learning Preferences or Rankings
- Machine Learning: Neural Networks
