5 papers
Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
Junli Shao, Jing Dong, Dingzhou Wang +3
With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model archit…
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 Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems
Haowei Yang, Yu Tian, Zhongheng Yang +3
With the rapid adoption of large language models (LLMs) in recommendation systems, the computational and communication bottlenecks caused by their massive parameter sizes and large…
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…