Something Similar: Exploring the Usefulness of an On-the-Spot Meal Recommendation System for Health Goal Attainment

Recommendation systems for nutrition management have become increasingly popular. Current solutions focus on optimizing for users taste preferences, often neglecting practical factors including personal priorities, existing plans, and situational constraints. We propose similarity-based meal recommendation - a novel approach that considers users existing meal plans and recommends similar meals that also align with their nutrition goals. This approach is designed to support meal decisions in on-the-spot, particularly when individuals are in constrained settings with limited options for adjustment (e.g. a cafeteria). We conducted a feasibility study with 15 participants to evaluate the utility of similarity-based recommendations in constrained settings and refine the system design. Findings show (1) a novel approach for similarity-based meal recommendations, (2) insights into the dimensions of similarity influencing actionability across diverse, and (3) opportunities to enhance user control over the system's design. The study contributes to the advancement of human-centered AI technologies in nutrition.