Putting food ads on a site devoted to recipes is a no-brainer, but Yummly is bringing some more complex data science to its platform than just keyword search ads. Yummly is applying the same algorithms it uses to parse recipes to its advertising, CEO David Feller said. So instead of popping off ads for boxed pastries whenever a user searches for “apple pie,” the engine can delve deeper.

An apple pie search might generate ads for the necessary ingredients such as for flour or apples, but it wouldn’t just select any variety of apples. Through recipe analysis, Yummly knows that apple pie is most often cooked using tart apples like Granny Smiths, so ads for red delicious apples just won’t do. Yummly can even infer what pairs well with a dish, Feller said.

“The data knows what we don’t know,” he said. “If somebody is looking for apple pie, Yummly knows that apple pie is complemented by ice cream, so we could give them ads for ice cream.”

Though those examples may seem rather simple, Yummly is launching with a limited number of advertisers and ad inventory. But the semantic search engine is growing quickly. In the last six months unique visitors on its site have nearly doubled, growing from 4 million in March to 7.5 million in September. That kind of growth won’t escape the notice of food brands. Feller thinks it won’t be long before Yummly can start delving down into very specific data to hone its ad targeting.

For instance, Yummly doesn’t just parse recipe ingredients. It extracts nutritional data and is even able to determine whether a dish or group of dishes is salty, sweet, savory or spicy from an analysis of its ingredients. Search options combined with histories generated by registered users tell Yummly about an individual cook’s preferences, whether he or she has food allergies, is on a fad diet or favors Asian over Mediterranean cuisines. Feller said all of these factors can be factored into ad targeting, and can be further refined by tracking seasonality of ingredients and even local weather conditions (stews and braises always go over well when its wet or cold).

The associations we make when craving food don’t always make logical sense. If I have a hankering chicken fried steak, it doesn’t mean I’m craving chicken or steak. Instead, I want comfort food, and Yummly would do much better to recommend me mac n’ cheese or meatloaf products and recipes. The ultimate goal, Feller said, is that Yummly will become wired the same ways are brains are to think about food

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The Series A round, led by Physic Ventures, is its first since Intel Capital, Harrison Metal Capital, First Round Capital and angel investors forked over $1.85 million in seed funding in 2010. All of Yummly’s original investors stuck around for another injection and were joined by Unilever Corporate Ventures and Harvard Common Press as well as Physic.

Feller and CTO (you guessed it, Chief Tasting Officer) Vadim Geshel used that seed funding to build Yummly’s core technology, which breaks down recipes and ingredients into raw culinary data, from which it extracts everything from calorie and saturated fat content to more conceptual notions of saltiness, sourness or bitterness. If it knows you’re a vegetarian, the Yummly search engine will weed out recipes that contain an ingredient that once walked, flew or swam. If it knows you like spicy foods, it will steer you to the recipes of pepper-loving regions of the world, rather than those of Northern Europe. Or if you’re on a diet, it can help you plan a meal down to the nearest carb or calorie.

But Feller said that the semantic technology Yummly has built has a lot more potential than the search engine lets on. Its current staff of 10 just doesn’t have the resources to build new products to tap into that potential. So Yummly plans to use its new $6 million to go on hiring spree and roll out new cooking applications and features. Some of those products will be the typical tablet apps and shopping list services we see proliferating on cooking sites and services today, but others will make more specific use of the vast food vocabulary Yummly has spent two years learning, Feller said.

“For instance, we’re able to understand when a recipe requires you to poach an egg,”  Feller said. “We can then push a video to you that shows just how to poach an egg.”

That may not sound like a big deal, but in the world of today’s Web, “poaching” is just a searchable term, not a series of cooking techniques that vary depending on the ingredients used or the style of cuisine employed. Poaching an egg in acidulated water is vastly different than poaching salmon in butter, just as caramelizing an onion uses a technique altogether different from caramelizing sugar. If Yummly can ferret out that context from raw recipe data then it will have accomplished something big.

First things first

Feller said Yummly first needs to build upon its core search engine, which he characterized as being more a proof-of-concept –- though its a proof-of-concept that gets 4 million unique visitors a month. It currently aggregates more than 600,000 recipes from 15 sites, ranging form the Food Network to AllRecipes, but it will expand its scope into further culinary corners of Web. It plans to add personalization features that will make Yummly more of a cooking portal than a search engine. And it has to find a revenue stream.

Ads have started showing up in Yummly searches, but Feller said the company has only been experimenting with them so far. Feller, however, has little doubt that Yummly can build a solid advertising business once it gets serious. The data it collects will make ad targeting far for refined than anything you see on AdWords.

“Say I’m an advertiser that wants to target vegetarians that like quinoa,” Feller said. “That’s an even more powerful concept when you consider that 10 percent of the GDP is food.”

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