KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction.
Ching-Hao Fan|Hao Zhou|Yao Sun|Geovanny Palomino Roldan|Olga Kokshagina|Marc Santolini|Lijing Wang
| Anthology ID: | DBLP:conf/mir/FanZSRKSW25 |
|---|---|
| Volume: | Proceedings of the 2025 International Conference on Multimedia Retrieval, ICMR 2025, Chicago, IL, USA, 30 June 2025 - 3 July 2025 |
| Year: | 2025 |
| Venue: | International Conference on Multimedia Retrieval (ICMR) |
| Publisher: | ACM |
| Pages: | 275-283 |
| URL: | https://doi.org/10.1145/3731715.3733368 |
| DOI: | https://doi.org/10.1145/3731715.3733368 |
| DBLP: | conf/mir/FanZSRKSW25 |
| BibTeX: |
@inproceedings{fan-2025-kegnn,
author = {Ching-Hao Fan and
Hao Zhou and
Yao Sun and
Geovanny Palomino Roldan and
Olga Kokshagina and
Marc Santolini and
Lijing Wang},
editor = {Zhongfei Zhang and
Elisa Ricci and
Yan Yan and
Liqiang Nie and
Vincent Oria and
Lamberto Ballan},
title = {{KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction}},
booktitle = {{Proceedings of the 2025 International Conference on Multimedia Retrieval, ICMR 2025, Chicago, IL, USA, 30 June 2025 - 3 July 2025}},
pages = {275--283},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3731715.3733368},
doi = {https://doi.org/10.1145/3731715.3733368}
}