Pairwise Fairness:基于pairwise提出一系列新颖的评估推荐公平性的指标,并表明pairwise fairness metric与排序效果直接相关并分析与poinwise fairness metrics的关系。 Pairwise Regularization:提出一个正则化方法在给定的指标上提高模型性能,同时对pointwise模型也有效。 Real-world Experiments:在大规模生产环境的推荐系统上...
Our findings reveal that fairness is a complex and often misunderstood concept, with a generally low level of public understanding regarding fairness metrics in recommender systems. This study highlights the need for enhanced information and education on algorithmic fairness to support informed decision-...
In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder recommender systems. Existing studies on provider fairness usually focused on designing proportion fairness (PF) metrics that first consider systematic ...
Enabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem, widely studied in both academia and industry. Current research has led to a variety of notions, metrics, and unfairness mitigation procedures. The evaluation of each procedure has been...
(201702) regularization use fairness metrics (e.g., value fairness) as fair regularization NIPS 2017 (201814) regularization use distribution matching and mutual information terms as regularization FAT* 2018 (201809) regularization add fairness regularization to SLIM FAT* 2018 (201811) regularization ind...
New Fairness Metrics for Recommendation that Embrace Differences We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lea... S Yao,B Huang 被引量: 5发表: 2017年 Fairness in Group Recommendations in the...
incorporates existing user browsing models that have previously been developed for information retrieval—to study fairness of content exposure to individual users. We extend their proposed framework to formalize a family of exposure fairness metrics that model the problem jointly from the per...
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. python machine-learning r ai deep-learning artificial-intelligence bias fairness bias-finder ibm-research discrimination bias-correction...
Ranking algorithms in recommender systems influence people to make decisions. Conventional ranking algorithms based on implicit feedback data aim to maximize the utility to users by capturing users’ preferences over items. However, these utility-focused algorithms tend to cause fairness issues that requi...
Article metrics 科大讯飞翻译(iFLYTEK Translation)Abstract Collaborative filtering (CF) techniques learn user and item embeddings from user-item interaction behaviors, and are commonly used in recommendation systems to help users find potentially desirable items. Most CF models optimize recommendation accuracy...