  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, and number of
demonstrations. As increasing the number of demonstrations in ICL does not
improve accuracy despite using a long prompt, we propose a novel method called
LLMSRec-Syn that incorporates multiple demonstration users into one aggregated
demonstration. Our experiments on three recommendation datasets show that
LLMSRec-Syn outperforms state-of-the-art LLM-based sequential recommendation
methods. In some cases, LLMSRec-Syn can perform on par with or even better than
supervised learning methods. Our code is publicly available at
https://github.com/demoleiwang/LLMSRec_Syn.
