Publications (122)
HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations
Sichun Luo, Xinyi Zhang, Yuanzhang Xiao +1
The recent popularity of edge devices and Artificial Intelligent of Things (AIoT) has driven a new wave of contextual recommendations, such as location based Point of Interest (PoI…
Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models
Shuqi Liu, Han Wu, Bowei He +3
Recent advances in large language models have led to numerous task-specialized fine-tuned variants, creating a need for efficient model merging techniques that preserve specialized…
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation
Sichun Luo, Yuanzhang Xiao, Linqi Song
Federated recommendation applies federated learning techniques in recommendation systems to help protect user privacy by exchanging models instead of raw user data between user dev…
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling
Yuxuan Yao, Han Wu, Mingyang Liu +5
Large language models (LLMs) exhibit varying strengths and weaknesses across different tasks, prompting recent studies to explore the benefits of ensembling models to leverage thei…
RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning
Sichun Luo, Jian Xu, Xiaojie Zhang +4
Large Language Models (LLMs) have been integrated into recommender systems to enhance user behavior comprehension. The Retrieval Augmented Generation (RAG) technique is further inc…
Activation-Guided Consensus Merging for Large Language Models
Yuxuan Yao, Shuqi Liu, Zehua Liu +6
Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based appro…