Improving Recommender Systems with Large Language Models

Sebastian Lubos*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Recommender systems offer an important technology aiding users in discovering relevant items among a multitude of available options. The emergence of Large Language Models (LLMs) enables a powerful opportunity to improve the performance and flexibility of recommender systems in different aspects. Those involve possibilities to enhance the item retrieval and ranking, explanation of recommendations, and generation of content to be recommended. The objective of my Ph.D. is to investigate those possibilities to identify and develop methods for improving different aspects of recommender systems using LLMs. The findings are expected to further develop the field of recommender systems and reveal novel opportunities for future research.

Original languageEnglish
Title of host publicationUMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York, NY
PublisherAssociation of Computing Machinery
Pages40-44
Number of pages5
ISBN (Electronic)9798400704666
DOIs
Publication statusPublished - 27 Jun 2024
Event32nd ACM Conference on User Modeling, Adaptation and Personalization: UMAP 2024 - Cagliari, Italy
Duration: 1 Jul 20244 Jul 2024
https://www.um.org/umap2024/

Conference

Conference32nd ACM Conference on User Modeling, Adaptation and Personalization
Abbreviated titleACM UMAP 2024
Country/TerritoryItaly
CityCagliari
Period1/07/244/07/24
Internet address

Keywords

  • decision-making
  • explanations
  • generation
  • large language model
  • recommender systems

ASJC Scopus subject areas

  • Software

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