1 citations · 1 across the 8 of their papers we have counts for
9 papers
CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition
Junjie Meng, Ranxu Zhang, Zi-an Zhang +6
Forecasting in large-scale e-commerce marketplaces is increasingly required to support planning: merchants need to evaluate sales outcomes under future action sequences such as bud…
Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media
Yijie Xu, Chao Wang, Hui Xiong
The rapid growth of social media has greatly influenced political discourse, highlighting the need to understand individual political ideologies and their temporal dynamics. This t…
Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models
Yongwen Ren, Chao Wang, Peng Du +3
Recent advances in pretrained language models (PLMs) have significantly improved conversational recommender systems (CRS), enabling more fluent and context-aware interactions. To f…
Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs
Haoran Xin, Ying Sun, Chao Wang +3
Despite the success of recommender systems in alleviating information overload, fairness issues have raised concerns in recent years, potentially leading to unequal treatment for c…
LLMs as Better Recommenders with Natural Language Collaborative Signals: A Self-Assessing Retrieval Approach
Haoran Xin, Ying Sun, Chao Wang +2
Incorporating collaborative information (CI) effectively is crucial for leveraging LLMs in recommendation tasks. Existing approaches often encode CI using soft tokens or abstract i…
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models
Rui Cai, Chao Wang, Qianyi Cai +2
Knowledge Graph-based recommendations have gained significant attention due to their ability to leverage rich semantic relationships. However, constructing and maintaining Knowledg…
DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives
Leilei Ding, Dazhong Shen, Chao Wang +3
Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node informa…
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Wei Wu, Chao Wang, Dazhong Shen +3
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture colla…
Graph Signal Diffusion Model for Collaborative Filtering
Yunqin Zhu, Chao Wang, Qi Zhang +1
Collaborative filtering is a critical technique in recommender systems. It has been increasingly viewed as a conditional generative task for user feedback data, where newly develop…