THGFormer: Time-Aware Hypergraph Learning for Multimodal Social Media Popularity Prediction (Student Abstract)
Jienan Zhang, Jie Liu, Zhangtao Cheng, Xovee Xu, Fang Liu, Ting Zhong, Kunpeng Zhang
Abstract
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.
BibTeX
@article{Zhang_Liu_Cheng_Xu_Liu_Zhong_Zhang_2024, title={THGFormer: Time-Aware Hypergraph Learning for Multimodal Social Media Popularity Prediction (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30534}, DOI={10.1609/aaai.v38i21.30534}, abstractNote={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.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhang, Jienan and Liu, Jie and Cheng, Zhangtao and Xu, Xovee and Liu, Fang and Zhong, Ting and Zhang, Kunpeng}, year={2024}, month={Mar.}, pages={23705-23706} }