THGFormer: Time-Aware Hypergraph Learning for Multimodal Social Media Popularity Prediction (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v38i21.30534Keywords:
Multimodal Learning, Time-aware Hypergraph, Hypergraph Learning, Social Media Popularity PredictionAbstract
Social media popularity prediction of multimodal user-generated content (UGC) is a crucial task for many real-world applications. However, existing efforts are often limited by missing inter-instance correlations and UGC temporal patterns. To address these issues, we propose a novel time-aware hypergraph Transformer framework, THGFormer. It fully represents inter-instance and intra-instance relations by hypergraphs, captures the temporal dependencies with a time encoder, and enhances UGC's representations via a neighborhood knowledge aggregation. Extensive experiments conducted on two real-world datasets demonstrate that THGFormer outperforms state-of-the-art popularity prediction models across several settings.Downloads
Published
2024-03-24
How to Cite
Zhang, J., Liu, J., Cheng, Z., Xu, X., Liu, F., Zhong, T., & Zhang, K. (2024). THGFormer: Time-Aware Hypergraph Learning for Multimodal Social Media Popularity Prediction (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23705-23706. https://doi.org/10.1609/aaai.v38i21.30534
Issue
Section
AAAI Student Abstract and Poster Program