Sequential Recommendation with Latent Relations Based on Large Language Models - M1.6

Explore a cutting-edge conference talk on sequential recommendation systems utilizing large language models. Delve into the innovative approach presented by researchers Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, and Min Zhang. Learn how they leverage latent relations within large language models to enhance sequential recommendation algorithms. Gain insights into the intersection of recommender systems and natural language processing, and discover potential applications for improving user experiences in various digital platforms. This 14-minute presentation, part of the RecSys and LLMs session at SIGIR 2024, offers a concise yet comprehensive overview of this promising research direction in the field of information retrieval and recommendation systems.

SIGIR 2024 M1.6 [fp] Sequential Recommendation with Latent Relations based on Large Language Model