A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915709540368384 |
|---|---|
| author | Tessa, Melissa Cidjeu, Diderot D. Carli, Rachele Abchiche, Sarah Aldarwishd, Ahmad Tchappi, Igor Najjar, Amro |
| author_facet | Tessa, Melissa Cidjeu, Diderot D. Carli, Rachele Abchiche, Sarah Aldarwishd, Ahmad Tchappi, Igor Najjar, Amro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02374 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models Tessa, Melissa Cidjeu, Diderot D. Carli, Rachele Abchiche, Sarah Aldarwishd, Ahmad Tchappi, Igor Najjar, Amro Information Retrieval 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. |
| title | A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2601.02374 |