you are what you eat! tracking health through recipe interactions
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You are What You Eat! Tracking Health Through Recipe Interac8ons
Alan Said TU Del? @alansaid
Alejandro Bellogín Universidad Autónoma de Madrid
@abellogin
RSWeb 2014
Outline • Recommenda8on contexts • Food-‐oriented recommender systems • Linking recipe interac8ons to health
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The different contexts of recommender systems -‐ Consump8on costs -‐ Product longevity -‐ Device
-‐ Health aspects <-‐ Food -‐ “offline” products
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Effects of a recommenda4on Online -‐ Discontent user -‐ Churn -‐ Business effects
Offline -‐ Direct effect on health -‐ Poten8al long-‐term effects
Recommenda8on of tangible items should take into considera8on the effect the item has on the consumer
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Real-‐world recommenda4on effects on users Social – Allrecipes.com Health – County Health Rankings Research Ques4on: Can we iden8fy unhealthy users based on their interac8on pa_erns?
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Allrecipes Data: -‐ 170k user profiles -‐ 54k recipes -‐ 8k ingredients -‐ 17m user-‐recipe interac8ons
Map users onto coun8es Map ingredient usage onto coun8es Map health data onto ingredient usage
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County Health Rankings Health rankings for 3400 US coun8es -‐ Obesity -‐ Heart disease -‐ Premature death -‐ Adult smoking -‐ Etc.
Iden8fy top 5 most/least obese coun8es using County Health Rankings www.countyhealthrankings.org
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The popular ingredient problem
Most used ingredients 51,04% Salt 33,72% Bu_er 30,67% Sugar 27,25% Eggs 26,14% Flour 23,93% Onions 22,79% Garlic 21,96% Water 20,65% Pepper 14,96% Milk 14,85% Vanilla 14,07% Olive oil 12,54% Brown sugar 10,20% Chicken 9,81% Cinnamon 7,96% Parmesan 7,89% Baking soda 7,74% Garlic powder 7,29% Vegetable oil 6,81% Cheddar cheese
55,30% Salt 32,92% Bu_er 31,01% Sugar 27,31% Garlic 26,77% Eggs 25,68% Flour 24,86% Onions 21,54% Water 21,42% Pepper 18,04% Olive oil 14,52% Vanilla 13,23% Milk 12,56% Brown sugar 10,00% Cinnamon 8,75% Baking soda 8,70% Chicken 8,25% Parmesan 7,61% Lemons 7,41% Vegetable oil 6,75% Garlic powder
Low obesity High obesity
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What now?
T-‐test on 110 most used ingredients (% of recipes) in obese and “slim” coun8es shows
p<0.05
Can we use this informa8on to infer the health of a user based on their recipe usage? Yes (we think so)
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Future work • Can we es8mate other health aspects? (diabetes,
smoking, heart disease, premature death, etc.) • Fine-‐grained analysis of recipes and ingredients
– Average opinion of recipes with certain ingredients, e.g. alcohol, candy, etc.
– Geographical recipe and ingredient trends • Recommending healthier alterna8ves to people at risk • Find a healthy “friend” to follow for inspira8on • Linking of cooking interests to health
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Resources • h_p://github.com/alansaid/RecipeCrawler • h_p://github.com/alansaid/RecipeParser
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THANKS! Ques8ons?
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• h_ps://www.flickr.com/photos/hotreactor/36171416/ • h_ps://www.flickr.com/photos/foodswings/4590408656 • h_ps://www.flickr.com/photos/atmtx/4294693128 • h_ps://www.flickr.com/photos/tarale/6689005901 • h_ps://www.flickr.com/photos/stria8c/131012552
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