ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems.
Yi Zhang|Ting Bai|Yiwen Zhang|Ting Bai|Yiwen Zhang|Kai Zheng|Tong Chen|Hongzhi Yin
| Anthology ID: | DBLP:conf/sigir/ZhangZZCY26 |
|---|---|
| Volume: | Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2026, MelbourneVICAustralia, July 20-24, 2026 |
| Year: | 2026 |
| Venue: | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) |
| Publisher: | ACM |
| Pages: | 2431-2441 |
| URL: | https://doi.org/10.1145/3805712.3809600 |
| DOI: | https://doi.org/10.1145/3805712.3809600 |
| DBLP: | conf/sigir/ZhangZZCY26 |
| BibTeX: |
@inproceedings{zhang-2026-promax,
author = {Yi Zhang and
Ting Bai and
Yiwen Zhang and
Ting Bai and
Yiwen Zhang and
Kai Zheng and
Tong Chen and
Hongzhi Yin},
editor = {Hannah Bast and
Alistair Moffat and
Falk Scholer and
Marc Najork and
Min Zhang and
Hannah Bast and
Marc Najork and
Min Zhang},
title = {{ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems}},
booktitle = {{Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2026, MelbourneVICAustralia, July 20-24, 2026}},
pages = {2431--2441},
publisher = {ACM},
year = {2026},
url = {https://doi.org/10.1145/3805712.3809600},
doi = {https://doi.org/10.1145/3805712.3809600}
}