Experimentation with fairness-aware recommendation using librec-auto: hands-on tutorial
Experimentation with fairness-aware recommendation using librec-auto: hands-on tutorial
复制标题
使用 librec-auto 进行公平感知推荐实验：实践教程
DOI：
10.1145/3351095.3375670
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Sonboli, Nasim
中科院分区：
文献类型：
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作者：
Burke, Robin;Mansoury, Masoud;Sonboli, Nasim
机器学习公平性领域已经开发了用于实验分类算法的指标、方法和数据集。然而，在个性化推荐系统领域缺乏相应的研究。这个180分钟的实践教程将向参与者介绍公平意识推荐的概念，以及评估推荐公平性的指标和方法。参与者还将获得使用librec-auto脚本平台使用LibRec推荐系统进行公平意识推荐实验的实践经验，并学习配置自己的实验，整合自己的数据集以及设计自己的算法和指标所需的步骤。
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 librec-auto scripting platform, and learn the steps required to configure their own experiments, incorporate their own data sets, and design their own algorithms and metrics.