CrowdGP: a Gaussian Process Model for Inferring Relevance from Crowd Annotations.
Dan Li|Zhaochun Ren|Evangelos Kanoulas
| Anthology ID: | DBLP:conf/www/0015RK21 |
|---|---|
| Volume: | WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021. |
| Year: | 2021 |
| Venue: | The Web Conference (WWW) |
| Publisher: | ACM / IW3C2 |
| Pages: | 1821-1832 |
| URL: | https://doi.org/10.1145/3442381.3450047 |
| DOI: | https://doi.org/10.1145/3442381.3450047 |
| DBLP: | conf/www/0015RK21 |
| BibTeX: |
@inproceedings{li-2021-crowdgp,
author = {Dan Li and
Zhaochun Ren and
Evangelos Kanoulas},
editor = {Jure Leskovec and
Marko Grobelnik and
Marc Najork and
Jie Tang and
Leila Zia},
title = {{CrowdGP: a Gaussian Process Model for Inferring Relevance from Crowd Annotations}},
booktitle = {{WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021}},
pages = {1821--1832},
publisher = {ACM / IW3C2},
year = {2021},
url = {https://doi.org/10.1145/3442381.3450047},
doi = {https://doi.org/10.1145/3442381.3450047}
}