Learning Sequential Correlation for User Generated Textual Content Popularity Prediction

Learning Sequential Correlation for User Generated Textual Content Popularity Prediction

Wen Wang, Wei Zhang, Jun Wang, Junchi Yan, Hongyuan Zha

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 1625-1631. https://doi.org/10.24963/ijcai.2018/225

Popularity prediction of user generated textual content is critical for prioritizing information in the web, which alleviates heavy information overload for ordinary readers. Most previous studies model each content instance separately for prediction and thus overlook the sequential correlations between instances of a specific user. In this paper, we go deeper into this problem based on the two observations for each user, i.e., sequential content correlation and sequential popularity correlation. We propose a novel deep sequential model called User Memory-augmented recurrent Attention Network (UMAN). This model encodes the two correlations by updating external user memories which is further leveraged for target text representation learning and popularity prediction. The experimental results on several real-world datasets validate the benefits of considering these correlations and demonstrate UMAN achieves best performance among several strong competitors.
Keywords:
Humans and AI: Personalization and User Modeling
Machine Learning: Recommender Systems