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PGRank
2016
Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion
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Point-of-interest (POI) recommendation has become more and more important, since it could discover user behavior pattern and find interesting venues for them. ...
To address this problem, we propose a rank-based method, PGRank, which integrates user geographical preference and latent preference into Bayesian personalized ranking framework. ...
information [1] . (3)BPR: a traditional pair-wise framework for recommendation [3] . (4)PGRank: Personalized Geographical Ranking is our proposed model described in Section 2. ...
doi:10.1145/2872518.2889378
dblp:conf/www/YingCXW16
fatcat:xitlbj4tvzbxtlpexwn7vtt6xe
Algorithmic Fairness Datasets: the Story so Far
[article]
2022
arXiv
pre-print
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Secondly, we document and summarize hundreds of available alternatives, annotating their domain and supported fairness tasks, along with additional properties of interest for fairness researchers. ...
We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel resources. ...
Acknowledgements The authors would like to thank the following researchers and dataset creators for the useful feedback on the data briefs: Alain Barrat, Luc Behaghel, Asia Biega, Marko Bohanec, Chris ...
arXiv:2202.01711v2
fatcat:5hf4a42pubc5vnt7tw3al4m5bq
Algorithmic Fairness Datasets: the Story so Far
[article]
2022
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
of automated decision-making for different populations. ...
We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel datasets. ...
Acknowledgements The authors would like to thank the following researchers and dataset creators for the useful feedback on the data briefs: Alain Barrat, Luc ...
doi:10.48550/arxiv.2202.01711
fatcat:mav36x3w5namjhurzpevtsmsju