TY - JOUR

T1 - Yum-Me

T2 - A personalized nutrient-based meal recommender system

AU - Yang, Longqi

AU - Hsieh, Cheng Kang

AU - Yang, Hongjian

AU - Pollak, John P.

AU - Dell, Nicola

AU - Belongie, Serge

AU - Cole, Curtis

AU - Estrin, Deborah

N1 - Funding Information: This work is funded through Awards from NSF (#1344587, #1343058) and NIH (#1U54EB020404); as well as gift funding from AOL, RWJF, UnitedHealth Group, Google, and Adobe. Publisher Copyright: Copyright 2017 is held by the owner/author(s). Publication rights licensed to ACM.

PY - 2017/4

Y1 - 2017/4

N2 - Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people's food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me, a personalized nutrient-based meal recommender system designed to meet individuals' nutritional expectations, dietary restrictions, and fine-grained food preferences. Yum-me enables a simple and accurate food preference profiling procedure via a visual quiz-based user interface and projects the learned profile into the domain of nutritionally appropriate food options to find ones that will appeal to the user.We present the design and implementation of Yum-me and further describe and evaluate two innovative contributions. The first contriution is an open source state-of-the-art food image analysis model, named FoodDist. We demonstrate FoodDist's superior performance through careful benchmarking and discuss its applicability across a wide array of dietary applications. The second contribution is a novel online learning framework that learns food preference from itemwise and pairwise image comparisons. We evaluate the framework in a field study of 227 anonymous users and demonstrate that it outperforms other baselines by a significant margin. We further conducted an end-to-end validation of the feasibility and effectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63%.

AB - Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people's food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me, a personalized nutrient-based meal recommender system designed to meet individuals' nutritional expectations, dietary restrictions, and fine-grained food preferences. Yum-me enables a simple and accurate food preference profiling procedure via a visual quiz-based user interface and projects the learned profile into the domain of nutritionally appropriate food options to find ones that will appeal to the user.We present the design and implementation of Yum-me and further describe and evaluate two innovative contributions. The first contriution is an open source state-of-the-art food image analysis model, named FoodDist. We demonstrate FoodDist's superior performance through careful benchmarking and discuss its applicability across a wide array of dietary applications. The second contribution is a novel online learning framework that learns food preference from itemwise and pairwise image comparisons. We evaluate the framework in a field study of 227 anonymous users and demonstrate that it outperforms other baselines by a significant margin. We further conducted an end-to-end validation of the feasibility and effectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63%.

KW - Food preferences

KW - Nutrient-based meal recommendation

KW - Online learning

KW - Personalization

KW - Visual interface

UR - http://www.scopus.com/inward/record.url?scp=85026445680&partnerID=8YFLogxK

U2 - 10.1145/3072614

DO - 10.1145/3072614

M3 - Journal article

AN - SCOPUS:85026445680

VL - 36

JO - ACM Transactions on Information Systems

JF - ACM Transactions on Information Systems

SN - 1046-8188

IS - 1

M1 - 7

ER -