8 citations · 28 across the 10 of their papers we have counts for
10 papers
Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation
Xiaolong Liu, Liangwei Yang, Zhiwei Liu +4
Personalized recommender systems aim to predict users' preferences for items. It has become an indispensable part of online services. Online social platforms enable users to form g…
A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder
Zheng Liu, Xiaohan Li, Philip Yu
The fairness issue of clinical data modeling, especially on Electronic Health Records (EHRs), is of utmost importance due to EHR's complex latent structure and potential selection…
Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs
Jiao Chen, Luyi Ma, Xiaohan Li +7
Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complement…
Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation
Xiaohan Li, Yuqing Liu, Zheng Liu +1
Session-based Recommendation (SBR) is to predict users' next interested items based on their previous browsing sessions. Existing methods model sessions as graphs or sequences to e…
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
Xiaohan Li, Zheng Liu, Luyi Ma +4
Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequ…
Mitigating Health Disparities in EHR via Deconfounder
Zheng Liu, Xiaohan Li, Philip Yu
Health disparities, or inequalities between different patient demographics, are becoming crucial in medical decision-making, especially in Electronic Health Record (EHR) predictive…