  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.
