1 Introduction Large language models(LLMs)have achieved remarkable progress in the field of natural language processing(NLP),showing impressive abilities to generate human-like texts for a broad range of tasks[1].Cons...1 Introduction Large language models(LLMs)have achieved remarkable progress in the field of natural language processing(NLP),showing impressive abilities to generate human-like texts for a broad range of tasks[1].Consequently,recent works start to investigate the application of LLMs in recommender systems.They adopt LLMs for various recommendation tasks,and show promising performance from different aspects(e.g.,user profiling).In this letter,we mainly focus on promoting the sample efficiency of recommender systems by involving large language models.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62177033).
文摘1 Introduction Large language models(LLMs)have achieved remarkable progress in the field of natural language processing(NLP),showing impressive abilities to generate human-like texts for a broad range of tasks[1].Consequently,recent works start to investigate the application of LLMs in recommender systems.They adopt LLMs for various recommendation tasks,and show promising performance from different aspects(e.g.,user profiling).In this letter,we mainly focus on promoting the sample efficiency of recommender systems by involving large language models.
