103 citations · 168 across the 16 of their papers we have counts for
16 papers
Federated Conversational Recommender System
Allen Lin, Jianling Wang, Ziwei Zhu +1
Conversational Recommender Systems (CRSs) have become increasingly popular as a powerful tool for providing personalized recommendation experiences. By directly engaging with users…
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…
Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model
Zhuoer Wang, Yicheng Wang, Ziwei Zhu +1
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…
CoPT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning
Xiangjue Dong, Ziwei Zhu, Zhuoer Wang +2
Pre-trained Language Models are widely used in many important real-world applications. However, recent studies show that these models can encode social biases from large pre-traini…
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts
Xiangjue Dong, Yun He, Ziwei Zhu +1
A key component of modern conversational systems is the Dialogue State Tracker (or DST), which models a user's goals and needs. Toward building more robust and reliable DSTs, we in…
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…
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems
Allen Lin, Jianling Wang, Ziwei Zhu +1
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 e…
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…
Session-based Recommendation with Hypergraph Attention Networks
Jianling Wang, Kaize Ding, Ziwei Zhu +1
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms. A critical challenge is to accurately model user in…
End-to-end Learning for Fair Ranking Systems
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck +1
The learning-to-rank problem aims at ranking items to maximize exposure of those most relevant to a user query. A desirable property of such ranking systems is to guarantee some no…
Fairness-aware Personalized Ranking Recommendation via Adversarial Learning
Ziwei Zhu, Jianling Wang, James Caverlee
Recommendation algorithms typically build models based on historical user-item interactions (e.g., clicks, likes, or ratings) to provide a personalized ranked list of items. These…
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition
Yun He, Ziwei Zhu, Yin Zhang +2
Knowledge of a disease includes information of various aspects of the disease, such as signs and symptoms, diagnosis and treatment. This disease knowledge is critical for many heal…
Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation
Yun He, Haochen Chen, Ziwei Zhu +1
We propose PsiRec, a novel user preference propagation recommender that incorporates pseudo-implicit feedback for enriching the original sparse implicit feedback dataset. Three of…
Fairness-Aware Recommendation of Information Curators
Ziwei Zhu, Jianling Wang, Yin Zhang +1
This paper highlights our ongoing efforts to create effective information curator recommendation models that can be personalized for individual users, while maintaining important f…