103 citations · 159 across the 18 of their papers we have counts for
7 papers · 1 filter
Countering Mainstream Bias via End-to-End Adaptive Local Learning
Jinhao Pan, Ziwei Zhu, Jianling Wang +2
Collaborative filtering (CF) based recommendations suffer from mainstream bias -- where mainstream users are favored over niche users, leading to poor recommendation quality for ma…
Automated Data Denoising for Recommendation
Yingqiang Ge, Mostafa Rahmani, Athirai Irissappane +3
In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback. Due to the issue of data…
Enhancing User Personalization in Conversational Recommenders
Allen Lin, Ziwei Zhu, Jianling Wang +1
Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just…
Evolution of Filter Bubbles and Polarization in News Recommendation
Han Zhang, Ziwei Zhu, James Caverlee
Recent work in news recommendation has demonstrated that recommenders can over-expose users to articles that support their pre-existing opinions. However, most existing work focuse…
Towards Fair Conversational Recommender Systems
Allen Lin, Ziwei Zhu, Jianling Wang +1
Conversational recommender systems have demonstrated great success. They can accurately capture a user's current detailed preference -- through a multi-round interaction cycle -- t…
Evolution of Popularity Bias: Empirical Study and Debiasing
Ziwei Zhu, Yun He, Xing Zhao +1
Popularity bias is a long-standing challenge in recommender systems. Such a bias exerts detrimental impact on both users and item providers, and many efforts have been dedicated to…