LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation.
Yuhao Wang|Yichao Wang|Zichuan Fu|Xiangyang Li|Wanyu Wang|Yuyang Ye|Xiangyu Zhao|Huifeng Guo|Ruiming Tang
| Anthology ID: | DBLP:conf/cikm/00060FLWY0GT24 |
|---|---|
| Volume: | Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 |
| Year: | 2024 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 2472-2481 |
| URL: | https://doi.org/10.1145/3627673.3679743 |
| DOI: | https://doi.org/10.1145/3627673.3679743 |
| DBLP: | conf/cikm/00060FLWY0GT24 |
| BibTeX: |
@inproceedings{wang-2024-llm4msr,
author = {Yuhao Wang and
Yichao Wang and
Zichuan Fu and
Xiangyang Li and
Wanyu Wang and
Yuyang Ye and
Xiangyu Zhao and
Huifeng Guo and
Ruiming Tang},
editor = {Edoardo Serra and
Francesca Spezzano},
title = {{LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation}},
booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}},
pages = {2472--2481},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3627673.3679743},
doi = {https://doi.org/10.1145/3627673.3679743}
}