Tutorial on Large Language Models for Recommendation

Wenyue Hua, Lei Li, Shuyuan Xu, Li Chen, Yongfeng Zhang

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

Abstract

Foundation Models such as Large Language Models (LLMs) have significantly advanced many research areas. In particular, LLMs offer significant advantages for recommender systems, making them valuable tools for personalized recommendations. For example, by formulating various recommendation tasks such as rating prediction, sequential recommendation, straightforward recommendation, and explanation generation into language instructions, LLMs make it possible to build universal recommendation engines that can handle different recommendation tasks. Additionally, LLMs have a remarkable capacity for understanding natural language, enabling them to comprehend user preferences, item descriptions, and contextual information to generate more accurate and relevant recommendations, leading to improved user satisfaction and engagement. This tutorial introduces Foundation Models such as LLMs for recommendation. We will introduce how recommender system advanced from shallow models to deep models and to large models, how LLMs enable generative recommendation in contrast to traditional discriminative recommendation, and how to build LLM-based recommender systems. We will cover multiple perspectives of LLM-based recommendation, including data preparation, model design, model pre-training, fine-tuning and prompting, multi-modality and multi-task learning, as well as trustworthy perspectives of LLM-based recommender systems such as fairness and transparency.

Original languageEnglish
Title of host publicationProceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023
EditorsJie Zhang, Li Chen, Shlomo Berkovsky
PublisherAssociation for Computing Machinery (ACM)
Pages1281-1283
Number of pages3
ISBN (Print)9798400702419
DOIs
Publication statusPublished - 14 Sept 2023
Event17th ACM Conference on Recommender Systems, RecSys 2023 - , Singapore
Duration: 18 Sept 202322 Sept 2023
https://dl.acm.org/doi/proceedings/10.1145/3604915

Publication series

NameProceedings of the ACM Conference on Recommender Systems

Conference

Conference17th ACM Conference on Recommender Systems, RecSys 2023
Country/TerritorySingapore
Period18/09/2322/09/23
Internet address

Scopus Subject Areas

  • Computer Science Applications
  • Information Systems
  • Software
  • Control and Systems Engineering

User-Defined Keywords

  • Foundation Models
  • Large Language Models
  • Recommendation

Fingerprint

Dive into the research topics of 'Tutorial on Large Language Models for Recommendation'. Together they form a unique fingerprint.

Cite this