Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space.
Pengcheng Li|Runze Li|Qing Da|Anxiang Zeng|Lijun Zhang
| Anthology ID: | DBLP:conf/cikm/LiLDZZ20 |
|---|---|
| Volume: | CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020 |
| Year: | 2020 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 2605-2612 |
| URL: | https://doi.org/10.1145/3340531.3412713 |
| DOI: | https://doi.org/10.1145/3340531.3412713 |
| DBLP: | conf/cikm/LiLDZZ20 |
| BibTeX: |
@inproceedings{li-2020-improving,
author = {Pengcheng Li and
Runze Li and
Qing Da and
Anxiang Zeng and
Lijun Zhang},
editor = {Mathieu d'Aquin and
Stefan Dietze and
Claudia Hauff and
Edward Curry and
Philippe Cudr\'{e}-Mauroux},
title = {{Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space}},
booktitle = {{CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020}},
pages = {2605--2612},
publisher = {ACM},
year = {2020},
url = {https://doi.org/10.1145/3340531.3412713},
doi = {https://doi.org/10.1145/3340531.3412713}
}