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 language | English |
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Title of host publication | UMAP Adjunct '24 |
Subtitle of host publication | Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization |
Place of Publication | New York, NY |
Publisher | Association for Computing Machinery (ACM) |
Pages | 229-230 |
Number of pages | 2 |
ISBN (Electronic) | 979-8-4007-0466-6 |
DOIs | |
Publication status | Published - 2024 |
Event | 32nd ACM Conference on User Modeling, Adaptation and Personalization - Cagliari, Italy Duration: 1 Jul 2024 → 4 Jul 2024 Conference number: 32 https://www.um.org/umap2024/ |
Conference
Conference | 32nd ACM Conference on User Modeling, Adaptation and Personalization |
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Abbreviated title | UMAP 2024 |
Country/Territory | Italy |
City | Cagliari |
Period | 1/07/24 → 4/07/24 |
Internet address |
Keywords
- Context
- Context-awareness
- Personalization
- Recommender systems
- User intent