# Validation of a recommender system for prompting omitted foods in online dietary assessment surveys

## TL;DR

This study evaluates a data-driven recommender system for online dietary surveys, showing it can identify more omitted foods than human-coded prompts but with lower precision, suggesting room for improvement.

## Contribution

It introduces and validates a machine learning-based recommender for dietary recall prompts, enhancing automation and scalability over traditional expert-driven methods.

## Key findings

- Recommender captures more omitted foods than nutritionist prompts.
- Recommender has lower precision than hand-coded prompts.
- System shows promise for automated dietary assessment improvements.

## Abstract

Recall assistance methods are among the key aspects that improve the accuracy of online dietary assessment surveys. These methods still mainly rely on experience of trained interviewers with nutritional background, but data driven approaches could improve cost-efficiency and scalability of automated dietary assessment. We evaluated the effectiveness of a recommender algorithm developed for an online dietary assessment system called Intake24, that automates the multiple-pass 24-hour recall method. The recommender builds a model of eating behavior from recalls collected in past surveys. Based on foods they have already selected, the model is used to remind respondents of associated foods that they may have omitted to report. The performance of prompts generated by the model was compared to that of prompts hand-coded by nutritionists in two dietary studies. The results of our studies demonstrate that the recommender system is able to capture a higher number of foods omitted by respondents of online dietary surveys than prompts hand-coded by nutritionists. However, the considerably lower precision of generated prompts indicates an opportunity for further improvement of the system.