Explore a 15-minute conference presentation from SIGIR 2024 that delves into the innovative application of Large Language Models (LLMs) as planners for long-term recommendation systems. Learn how researchers Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, and Fuli Feng investigate the potential of LLMs to enhance recommendation systems by incorporating long-term planning capabilities. Discover how these models can be trained to understand user preferences and behavior patterns over extended periods, ultimately improving the quality and relevance of recommendations in various applications.
Large Language Models as Learnable Planners for Long-Term Recommendation
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 W1.2 [fp] Large Language Models are Learnable Planners for Long-Term Recommendation
Taught by
Association for Computing Machinery (ACM)
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