1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.IR2024
User-Creator Feature Polarization in Recommender Systems with Dual Influence
Tao Lin, Kun Jin, Andrew Estornell +3
Recommender systems serve the dual purpose of presenting relevant content to users and helping content creators reach their target audience. The dual nature of these systems natura…
cs.IR2024★ 1 cited
Measuring Fairness in Large-Scale Recommendation Systems with Missing Labels
Yulong Dong, Kun Jin, Xinghai Hu +1
In large-scale recommendation systems, the vast array of items makes it infeasible to obtain accurate user preferences for each product, resulting in a common issue of missing labe…
cs.LG2024
Addressing Polarization and Unfairness in Performative Prediction
Kun Jin, Tian Xie, Yang Liu +1
In many real-world applications of machine learning such as recommendations, hiring, and lending, deployed models influence the data they are trained on, leading to feedback loops…