Work

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

Year: 2025

Type: conference-paper

Abstract: 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.

Source: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA)

DOI: 10.1145/3706599.3719813

Authors: Pooja M. Desai, Ayush Raj, David Albers, Aparajita Kashyap, Lena Mamykina

Institutions: Columbia University Irving Medical Center, Columbia University, University of Colorado Anschutz

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDGs: Good health and well-being, Zero hunger

Open Access status: green

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, U.S. National Library of Medicine