16 citations · 21 across the 9 of their papers we have counts for
9 papers
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…
iAgent: LLM Agent as a Shield between User and Recommender Systems
Wujiang Xu, Yunxiao Shi, Zujie Liang +6
Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. However, th…
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain Recommendation
Li Wang, Qiang Wu, Min Xu
Cross-domain recommendation (CDR) aims to improve recommendation accuracy in sparse domains by transferring knowledge from data-rich domains. However, existing CDR approaches often…
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…
Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation
Li Wang, Shoujin Wang, Quangui Zhang +2
Cross-domain recommendation (CDR) aims to address the data-sparsity problem by transferring knowledge across domains. Existing CDR methods generally assume that the user-item inter…
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…
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…