排名(信息检索)
推荐系统
计算机科学
生成语法
生成模型
数据科学
人工智能
万维网
作者
Keqin Bao,Jizhi Zhang,Yang Zhang,Wenjie Wang,Fuli Feng,Xiangnan He
标识
DOI:10.1145/3624918.3629550
摘要
The powerful large language models (LLMs) have played a pivotal role in advancing recommender systems. Recently, in both academia and industry, there has been a surge of interest in developing LLMs for recommendation, referred to as LLM4Rec. This includes endeavors like leveraging LLMs for generative item retrieval and ranking, as well as the exciting possibility of building universal LLMs for diverse open-ended recommendation tasks. These developments hold the potential to reshape the traditional recommender paradigm, paving the way for the next-generation recommender systems.
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