211 citations · 527 across the 28 of their papers we have counts for
28 papers
Debiasing Recommendation by Learning Identifiable Latent Confounders
Qing Zhang, Xiaoying Zhang, Yang Liu +4
Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic st…
Disentangled Representation for Diversified Recommendations
Xiaoying Zhang, Hongning Wang, Hang Li
Accuracy and diversity have long been considered to be two conflicting goals for recommendations. We point out, however, that as the diversity is typically measured by certain pre-…
Integrating Voice-Based Machine Learning Technology into Complex Home Environments
Ye Gao, Jason Jabbour, Eunjung Ko +8
To demonstrate the value of machine learning based smart health technologies, researchers have to deploy their solutions into complex real-world environments with real participants…
MiddleGAN: Generate Domain Agnostic Samples for Unsupervised Domain Adaptation
Ye Gao, Zhendong Chu, Hongning Wang +1
In recent years, machine learning has achieved impressive results across different application areas. However, machine learning algorithms do not necessarily perform well on a new…
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
Nan Wang, Qifan Wang, Yi-Chia Wang +5
As language models become increasingly integrated into our digital lives, Personalized Text Generation (PTG) has emerged as a pivotal component with a wide range of applications. H…
Rethinking Conversational Recommendations: Is Decision Tree All You Need?
A S M Ahsan-Ul Haque, Hongning Wang
Conversational recommender systems (CRS) dynamically obtain the user preferences via multi-turn questions and answers. The existing CRS solutions are widely dominated by deep reinf…