A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models

TL;DR

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.

Abstract

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.

Figures (4)

• Figure 1: Overall description scheme.
• Figure 2: Average rating of models simple, contrastive and both explanations.
• Figure 3: Pourcentage of preferred models' contrastive explanations.
• Figure 4: Pourcentage of preferred models' plain explanations.