1 code implementation • 13 Apr 2024 • Jinhao Pan, Ziwei Zhu, Jianling Wang, Allen Lin, James Caverlee
In this paper, we identify two root causes of this mainstream bias: (i) discrepancy modeling, whereby CF algorithms focus on modeling mainstream users while neglecting niche users with unique preferences; and (ii) unsynchronized learning, where niche users require more training epochs than mainstream users to reach peak performance.
1 code implementation • 28 Oct 2023 • Zheyuan Liu, Guangyao Dou, Yijun Tian, Chunhui Zhang, Eli Chien, Ziwei Zhu
Exploring the full spectrum of trade-offs between privacy, model utility, and runtime efficiency is critical for practical unlearning scenarios.
1 code implementation • 26 Oct 2023 • Anjishnu Mukherjee, Chahat Raj, Ziwei Zhu, Antonios Anastasopoulos
Finally, we highlight the significance of these social biases and the new dimensions through an extensive comparison of embedding methods, reinforcing the need to address them in pursuit of more equitable language models.
1 code implementation • 19 Oct 2023 • Xiangjue Dong, Ziwei Zhu, Zhuoer Wang, Maria Teleki, James Caverlee
Pre-trained Language Models are widely used in many important real-world applications.
no code implementations • 19 Oct 2023 • Zhuoer Wang, Yicheng Wang, Ziwei Zhu, James Caverlee
Question generation is a widely used data augmentation approach with extensive applications, and extracting qualified candidate answers from context passages is a critical step for most question generation systems.
no code implementations • 23 Jul 2023 • Ziwei Zhu, Changhao Shan, Bihong Zhang, Jian Yu
We combine the methods of meta learning and freeze of model parameters, which makes the recognition performance more stable in different cases and the training faster about 20%.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
1 code implementation • 7 Jun 2023 • Xiangjue Dong, Yun He, Ziwei Zhu, James Caverlee
A key component of modern conversational systems is the Dialogue State Tracker (or DST), which models a user's goals and needs.
no code implementations • 13 Feb 2023 • Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee
Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience.
no code implementations • 26 Jan 2023 • Han Zhang, Ziwei Zhu, James Caverlee
However, most existing work focuses on a static setting or over a short-time window, leaving open questions about the long-term and dynamic impacts of news recommendations.
no code implementations • 16 Jan 2023 • Saptarshi Roy, Ambuj Tewari, Ziwei Zhu
Furthermore, we show that a margin condition depending on similar margin quantity and complexity measures is also necessary for model consistency of BSS.
no code implementations • 8 Aug 2022 • Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee
Conversational recommender systems have demonstrated great success.
no code implementations • 5 Aug 2022 • Allen Lin, Jianling Wang, Ziwei Zhu, James Caverlee
Conversational recommender systems (CRS) have shown great success in accurately capturing a user's current and detailed preference through the multi-round interaction cycle while effectively guiding users to a more personalized recommendation.
1 code implementation • 7 Jul 2022 • Ziwei Zhu, Yun He, Xing Zhao, James Caverlee
and how to debias in this long-term dynamic process?
no code implementations • 4 Jan 2022 • Wen Wang, Shihao Wu, Ziwei Zhu, Ling Zhou, Peter X. -K. Song
Fusing regression coefficients into homogenous groups can unveil those coefficients that share a common value within each group.
no code implementations • 28 Dec 2021 • Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms.
no code implementations • 21 Nov 2021 • James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Ziwei Zhu
The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints while allowing for fine control of the fairness-utility tradeoff.
1 code implementation • 14 Mar 2021 • Ziwei Zhu, Jianling Wang, James Caverlee
This is paper is an extended and reorganized version of our SIGIR 2020~\cite{zhu2020measuring} paper.
no code implementations • WSDM 2021 • Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, James Caverlee
This paper connects equal opportunity to popularity bias in implicit recommenders to introduce the problem of popularity-opportunity bias.
1 code implementation • EMNLP 2020 • Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, James Caverlee
Knowledge of a disease includes information of various aspects of the disease, such as signs and symptoms, diagnosis and treatment.
no code implementations • 28 Jun 2019 • Ziwei Zhu, Xudong Li, Mengdi Wang, Anru Zhang
We show that one can estimate the full transition model accurately using a trajectory of length that is proportional to the total number of states.
no code implementations • 9 Sep 2018 • Ziwei Zhu, Jianling Wang, Yin Zhang, James Caverlee
This paper highlights our ongoing efforts to create effective information curator recommendation models that can be personalized for individual users, while maintaining important fairness properties.
no code implementations • 12 Aug 2018 • Jianqing Fan, Kaizheng Wang, Yiqiao Zhong, Ziwei Zhu
Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance.