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cs.LG2025
RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders
Zhongheng Yang, Aijia Sun, Yushang Zhao +3
Conversational recommender systems (CRS) based on Large Language Models (LLMs) need to constantly be aligned to the user preferences to provide satisfying and context-relevant item…
cs.LG2025★ 1 cited
Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs
Yushang Zhao, Huijie Shen, Dannier Li +3
Generative, explainable, and flexible recommender systems, derived using Large Language Models (LLM) are promising and poorly adapted to the cold-start user situation, where there…
cs.LG2025
Research on Low-Latency Inference and Training Efficiency Optimization for Graph Neural Network and Large Language Model-Based Recommendation Systems
Yushang Zhao, Haotian Lyu, Yike Peng +3
The incessant advent of online services demands high speed and efficient recommender systems (ReS) that can maintain real-time performance along with processing very complex user-i…