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

TL;DR

This paper presents a food recommendation system that uses large language models and hybrid feature importance extraction to generate clear, trustworthy explanations for lay users, improving transparency and user trust.

Contribution

It introduces a novel hybrid approach combining LLMs with SHAP for explainable food recommendations tailored for lay users.

Findings

Provides more comprehensive explanations than existing methods.

Enhances user trust and transparency in food recommendations.

Demonstrates effectiveness through qualitative analysis.

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.