Hands-on Tutorial: Experimentation with fairness-aware recommendation using librec-auto

被引:2
作者
Burke, Robin Douglas [1 ]
Mansoury, Masoud [2 ]
Sonboli, Nasim [1 ]
机构
[1] Univ Colorado, Boulder, CO 80309 USA
[2] Eindhoven Univ Technol, Eindhoven, Netherlands
来源
FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY | 2020年
关键词
recommender systems; fairness; evaluation; software;
D O I
10.1145/3351095.3375670
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The field of machine learning fairness has developed metrics, methodologies, and data sets for experimenting with classification algorithms. However, equivalent research is lacking in the area of personalized recommender systems. This 180-minute hands-on tutorial will introduce participants to concepts in fairness-aware recommendation, and metrics and methodologies in evaluating recommendation fairness. Participants will also gain hands-on experience with conducting fairness-aware recommendation experiments with the LibRec recommendation system using the \libauto{} scripting platform, and learn the steps required to configure their own experiments, incorporate their own data sets, and design their own algorithms and metrics.
引用
收藏
页码:700 / 700
页数:1
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