The whole is better than the sum : Using aggregated demonstrations in in-context learning for sequential recommendation

• Main Authors: LEI, Wang, LIM, Ee-Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Large language models; LLMs; Sequential recommendation; Artificial Intelligence and Robotics; Computer Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/9786; https://ink.library.smu.edu.sg/context/sis_research/article/10786/viewcontent/2024.findings_naacl.56.pdf
• Institution: Singapore Management University

Large language models (LLMs) have shown excellent performance on various NLP tasks. To use LLMs as strong sequential recommenders, we explore the in-context learning approach to sequential recommendation. We investigate the effects of instruction format, task consistency, demonstration selection, an...