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Inf. Syst."],"published-print":{"date-parts":[[2018,1,31]]},"abstract":"<jats:p>\n                    Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people\u2019s 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\n                    <jats:italic>Yum-me<\/jats:italic>\n                    , a personalized nutrient-based meal recommender system designed to meet individuals\u2019 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\n                    <jats:italic>FoodDist<\/jats:italic>\n                    . We demonstrate FoodDist\u2019s 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%.\n                  <\/jats:p>","DOI":"10.1145\/3072614","type":"journal-article","created":{"date-parts":[[2017,7,17]],"date-time":"2017-07-17T08:20:12Z","timestamp":1500279612000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":131,"title":["Yum-Me"],"prefix":"10.1145","volume":"36","author":[{"given":"Longqi","family":"Yang","sequence":"first","affiliation":[{"name":"Cornell Tech, Cornell University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng-Kang","family":"Hsieh","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjian","family":"Yang","sequence":"additional","affiliation":[{"name":"Cornell Tech, Cornell University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John P.","family":"Pollak","sequence":"additional","affiliation":[{"name":"Cornell Tech, Cornell University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola","family":"Dell","sequence":"additional","affiliation":[{"name":"Cornell Tech, The Jacobs Institute, Cornell University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serge","family":"Belongie","sequence":"additional","affiliation":[{"name":"Cornell Tech, Cornell University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Curtis","family":"Cole","sequence":"additional","affiliation":[{"name":"Weill Cornell Medical College, Cornell University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deborah","family":"Estrin","sequence":"additional","affiliation":[{"name":"Cornell Tech, Cornell University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,7,17]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/ejcn.2011.75"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the ACM-SIAM Symposium on Discrete Algorithms.","author":"Arthur David","year":"2007","unstructured":"David Arthur and Sergei Vassilvitskii . 2007 . k-means++: The advantages of careful seeding . 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