5 papers
Efficient Cold-Start Recommendation via BPE Token-Level Embedding Initialization with LLM
Yushang Zhao, Xinyue Han, Qian Leng +3
The cold-start issue is the challenge when we talk about recommender systems, especially in the case when we do not have the past interaction data of new users or new items. Conten…
Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users
Haowei Yang, Yushang Zhao, Sitao Min +3
The cold-start user issue further compromises the effectiveness of recommender systems in limiting access to the historical behavioral information. It is an effective pipeline to o…
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…
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…
Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks
Yushang Zhao, Yike Peng, Dannier Li +3
With the rapid growth of fintech, personalized financial product recommendations have become increasingly important. Traditional methods like collaborative filtering or content-bas…