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 language | English |
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Title of host publication | UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization |
Place of Publication | New York, NY |
Publisher | Association of Computing Machinery |
Pages | 40-44 |
Number of pages | 5 |
ISBN (Electronic) | 9798400704666 |
DOIs | |
Publication status | Published - 27 Jun 2024 |
Event | 32nd ACM Conference on User Modeling, Adaptation and Personalization: UMAP 2024 - Cagliari, Italy Duration: 1 Jul 2024 → 4 Jul 2024 https://www.um.org/umap2024/ |
Conference
Conference | 32nd ACM Conference on User Modeling, Adaptation and Personalization |
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Abbreviated title | ACM UMAP 2024 |
Country/Territory | Italy |
City | Cagliari |
Period | 1/07/24 → 4/07/24 |
Internet address |
Keywords
- decision-making
- explanations
- generation
- large language model
- recommender systems
ASJC Scopus subject areas
- Software