16 citations · 21 across the 10 of their papers we have counts for
10 papers
MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models
Yunxiao Shi, Shuo Yang, Haimin Zhang +4
Neural Collaborative Filtering models are widely used in recommender systems but are typically trained under static settings, assuming fixed data distributions. This limits their a…
Enhancing News Recommendation with Hierarchical LLM Prompting
Hai-Dang Kieu, Delvin Ce Zhang, Minh Duc Nguyen +3
Personalized news recommendation systems often struggle to effectively capture the complexity of user preferences, as they rely heavily on shallow representations, such as article…
PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequence
Yunxiao Shi, Wujiang Xu, Zeqi Zhang +3
User profile embedded in the prompt template of personalized recommendation agents play a crucial role in shaping their decision-making process. High-quality user profiles are esse…
A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine
Yunxiao Shi, Min Xu, Haimin Zhang +2
Large language models (LLMs) and retrieval-augmented generation (RAG) techniques have revolutionized traditional information access, enabling AI agent to search and summarize infor…
Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction
Yunxiao Shi, Wujiang Xu, Haimin Zhang +2
Modeling high-order feature interactions is crucial for click-through rate (CTR) prediction, yet traditional approaches typically predefine a maximum interaction order and exhausti…
Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
Yunxiao Shi, Xing Zi, Zijing Shi +3
Retrieval-augmented generation (RAG) techniques leverage the in-context learning capabilities of large language models (LLMs) to produce more accurate and relevant responses. Origi…
Conditional Local Feature Encoding for Graph Neural Networks
Yongze Wang, Haimin Zhang, Qiang Wu +1
Graph neural networks (GNNs) have shown great success in learning from graph-based data. The key mechanism of current GNNs is message passing, where a node's feature is updated bas…
ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization
Yunxiao Shi, Xing Zi, Zijing Shi +3
Retrieval-augmented generation (RAG) for language models significantly improves language understanding systems. The basic retrieval-then-read pipeline of response generation has ev…
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation
Li Wang, Lei Sang, Quangui Zhang +2
Cross-domain recommendation (CDR) aims to enhance recommendation accuracy in a target domain with sparse data by leveraging rich information in a source domain, thereby addressing…
Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation
Li Wang, Min Xu, Quangui Zhang +2
Social recommendation systems face the problem of social influence bias, which can lead to an overemphasis on recommending items that friends have interacted with. Addressing this…