Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2024

Abstract

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.

Keywords

Large language models, LLMs, Sequential recommendation

Discipline

Artificial Intelligence and Robotics | Computer Sciences

Research Areas

Data Science and Engineering

Publication

Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024) : Mexico City, Mexico, June 16-21

First Page

876

Last Page

895

Identifier

10.18653/v1/2024.findings-naacl.56

Publisher

Association for Computational Linguistics

City or Country

Mexico City

Additional URL

https://doi.org/10.18653/v1/2024.findings-naacl.56

Download

Share

COinS