Algorythms can predict the future,
but unlike the Delphi oracle, they do it based on hard scientific methods,
instead of intestines and animal bones. Finding old friends on Facebook,
recomending books you might like on Amazon, or predicting the outcome of the
2012 presidential election – you name it, an algorythm does it.
So what about food? Could these same
math whizzes help us bake a better pumpkin pie or mix up a tastier batch of sweet
potatoes this Christmas? Lada Adamic,
a computer scientist at the University of Michigan and Facebook, thinks it just
might be possible. she and her team have come up with an algorithm to guess
how successful a recipe will turn out. And the math works surprisingly well. It
predicts with nearly 80 percent accuracy how many stars your mother's cranberry
recipe will receive on allrecipes.com.
Plus, it can recommend ingredient replacements to make your pie crust and
potatoes more healthful.
She and her team took nearly 50,000 recipes and 2 million reviews from
allrecipes.com and then hacked up an algorithm to extract out all the
ingredients, cooking methods and nutritional profiles. With just these items,
her algorithm could predict the recipe's rating with an accuracy of about 70
percent. But the magic happened when Adamic built a "social network"
for the ingredients. She looked at how often two ingredients appear in the same
recipes. Those that frequently show up together — milk and butter, nutmeg and
cinnamon, basil and rosemary — sit close to each other in the network, but
those that rarely appear in the same dish, such as coconut and parsley, are far
from each other.
Physicists at Harvard University performed a similar network analysis on ingredients' flavors, but Adamic took it a step further and integrated the data into a recipe prediction program.
Physicists at Harvard University performed a similar network analysis on ingredients' flavors, but Adamic took it a step further and integrated the data into a recipe prediction program.
Adamic's network analysis boosted the accuracy of her
recipe recommendations by about 10 percent. But it also revealed a
treasure-trove of information about the way Americans mix and match
ingredients, which ones we like to leave out or throw in extra.
Her algorithm analyzed reviewers' recommendations for customizing recipes, such as "I replaced the butter in the frosting by sour cream, just to soothe my conscience about all the fatty calories" and "This is a great recipe, but using fresh tomatoes only adds a few minutes to the prep time." Then the mathematics stitched together little clusters or communities of interchangeable foods and spices.
Her algorithm analyzed reviewers' recommendations for customizing recipes, such as "I replaced the butter in the frosting by sour cream, just to soothe my conscience about all the fatty calories" and "This is a great recipe, but using fresh tomatoes only adds a few minutes to the prep time." Then the mathematics stitched together little clusters or communities of interchangeable foods and spices.
The result is a list of recipe
replacements more comprehensive and scientifically accurate than anything
you'll find in the Joy of Cooking or online.
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