Are We Losing Interest in Context-Aware Recommender Systems?

Laurens Rook, Markus Zanker, Dietmar Jannach

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
16 Downloads (Pure)

Abstract

Contextual information is a prerequisite for timely offering of personalized decision support and recommendation. Yet, research on context-aware recommender systems (CARS) does not appear to be thriving, and finding public datasets containing context factors is a challenging task. We can make various assumptions about why this drop in research interest happened – be it ethical considerations or the popularity of opaque deep learning models that merely consider context in an implicit way. This is an unwelcome development. We argue that continued effort must be put on the creation of suitable datasets. Furthermore, we see significant opportunities in the development of next-generation CARS in the space of interactive AI assistants powered by Large Language Models.
Original languageEnglish
Title of host publicationUMAP Adjunct '24
Subtitle of host publicationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages229-230
Number of pages2
ISBN (Electronic)979-8-4007-0466-6
DOIs
Publication statusPublished - 2024
Event32nd ACM Conference on User Modeling, Adaptation and Personalization - Cagliari, Italy
Duration: 1 Jul 20244 Jul 2024
Conference number: 32
https://www.um.org/umap2024/

Conference

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

Keywords

  • Context
  • Context-awareness
  • Personalization
  • Recommender systems
  • User intent

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