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PGRank

Haochao Ying, Liang Chen, Yuwen Xiong, Jian Wu
2016 Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion  
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]

Alessandro Fabris, Stefano Messina, Gianmaria Silvello, Gian Antonio Susto
2022 arXiv   pre-print
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]

Alessandro Fabris, Stefano Messina, Gianmaria Silvello, Gian Antonio Susto
2022
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