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} }