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Persistent Identifier
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perma:LIST.YD8LTU |
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Publication Date
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2026-07-06 |
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Title
| A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models [* Cross-Reference *] |
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Other Identifier
| https://doi.org/10.1016/j.procs.2026.04.093
OpenAlex ID: https://openalex.org/W7163225866 |
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Author
| Melissa Tessa (University of Luxembourg) - ORCID: https://orcid.org/0009-0008-7525-5522
Diderot D. Cidjeu (Institut de Recherche Pour le Développement)
Rachele Carli (Umeå University) - ORCID: https://orcid.org/0000-0002-8689-285X
Sarah Abchiche (École Nationale Supérieure d'Informatique) - ORCID: https://orcid.org/0009-0008-1097-8527
Ahmad Aldarwish (Prince Mohammed bin Abdulaziz Hospital)
Igor Tchappi (University of Luxembourg)
Amro Najjar (Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0001-7784-6176 |
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Point of Contact
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Use email button above to contact.
LIST QDKM (LIST) |
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Description
| Large Language Models (LLM) have experienced strong development in recent years, with varied applications. This paper uses LLMs to develop a post-hoc process that provides more elaborated explanations of the results of food recommendation systems. By combining LLM with a hybrid extraction of key variables using SHAP, we obtain dynamic, convincing and more comprehensive explanations to lay user, compared to those in the literature. This approach enhances user trust and transparency by making complex recommendation outcomes easier to understand for a lay user. (2026-01-01)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.1016/j.procs.2026.04.093 for the original and latest version of the publication*** (2026-07-01) |
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Subject
| Computer and Information Science; Physics |
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Keyword
| Recommender system
Transparency (behavior)
Key (lock)
Process (computing)
Feature (linguistics) |
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Topic Classification
| Explainable Artificial Intelligence (XAI)
Sentiment Analysis and Opinion Mining
Recommender Systems and Techniques |
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Deposit Date
| 2026-01-01 |
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Data Type
| Article |
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Data Source
| Procedia Computer Science |