THGFormer: Time-Aware Hypergraph Learning for Multimodal Social Media Popularity Prediction (Student Abstract)
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.
Cite
Text
Zhang et al. "THGFormer: Time-Aware Hypergraph Learning for Multimodal Social Media Popularity Prediction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30534Markdown
[Zhang et al. "THGFormer: Time-Aware Hypergraph Learning for Multimodal Social Media Popularity Prediction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/zhang2024aaai-thgformer/) doi:10.1609/AAAI.V38I21.30534BibTeX
@inproceedings{zhang2024aaai-thgformer,
title = {{THGFormer: Time-Aware Hypergraph Learning for Multimodal Social Media Popularity Prediction (Student Abstract)}},
author = {Zhang, Jienan and Liu, Jie and Cheng, Zhangtao and Xu, Xovee and Liu, Fang and Zhong, Ting and Zhang, Kunpeng},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23705-23706},
doi = {10.1609/AAAI.V38I21.30534},
url = {https://mlanthology.org/aaai/2024/zhang2024aaai-thgformer/}
}