Record link: 
http://hdl.handle.net/20.500.12708/204479
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Title: 
Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures
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Citation: 
Kolb, T. E. (2024). Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures. In RecSys ’24: Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1388–1394). Association for Computing Machinery. https://doi.org/10.1145/3640457.3688027
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Publisher DOI: 
10.1145/3640457.3688027
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Kolb, Thomas Elmar 
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Organisational Unit: 
E194-04 - Forschungsbereich Data Science 
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Published in: 
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems 
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ISBN: 
979-8-4007-0505-2
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DOI of the book: 
10.1145/3640457
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Date (published): 
2024
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Event name: 
RecSys '24: 18th ACM Conference on Recommender Systems 
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Event date: 
14-Oct-2024 - 18-Oct-2024
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Event place: 
Bari, Italy
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Number of Pages: 
7
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Publisher: 
Association for Computing Machinery, New York, NY, United States
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Peer reviewed: 
Yes
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Keywords: 
cross-domain recommender systems; beyond-accuracy; LLMs; bias
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Project title: 
Christian Doppler Labor für Weiterentwicklung des State-of-the-Art von Recommender-Systemen in mehreren Domänen: CDL Neidhardt (Christian Doppler Forschungsgesells) 
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Research Areas: 
Information Systems Engineering: 100%
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Science Branch: 
1020 - Informatik: 100%
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Appears in Collections:
Conference Paper

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