Attentional Image Retweet Modeling via Multi-Faceted Ranking Network Learning

Attentional Image Retweet Modeling via Multi-Faceted Ranking Network Learning

Zhou Zhao, Lingtao Meng, Jun Xiao, Min Yang, Fei Wu, Deng Cai, Xiaofei He, Yueting Zhuang

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3184-3190. https://doi.org/10.24963/ijcai.2018/442

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