There Are Three Kinds of "Similar" in Food. Most Recipes Only Know One.

When a recipe site tells you "if you like basil, try parsley," it's not wrong. But it's answering a much smaller question than it thinks it is.

Basil and parsley are similar the way coworkers are similar - they show up in the same places, they run in the same circles, they're comfortable on the same plate. That's useful. It's also only one definition of similar.

A group of researchers recently built something more interesting: a map of ingredients that can answer three different versions of that question, depending on what you actually want to know.

The Three Maps

The paper is called Epicure. The researchers trained three separate ingredient models on 4 million recipes, each one tuned to emphasize a different signal:

• Cooc - "What do people cook this with?" Pure recipe-context. Garlic lives next to olive oil, onion, and chicken because that's where it actually shows up on the plate.
• Chem - "What does this taste like, molecularly?" Garlic ends up near asafoetida and leek - ingredients you might never combine in practice, but that share the same sulfur-heavy chemical signature.
• Core - A hybrid. Recipe context plus chemistry, blended together.

Same training process, same model architecture. Just a different definition of "near."

The result is that you can ask the system the same question - what's similar to garlic? - and get genuinely different, genuinely useful answers depending on which lens you look through.

Why That's Actually Useful

Think about the last time you needed to substitute an ingredient. If you're out of soy sauce, the recipe-context model helps: sesame oil, oyster sauce, and miso are in that neighborhood. They'll hold the dish together because chefs have always used them together.

But if you're trying to understand why a dish tastes the way it does - or if you want to recreate a flavor profile with completely different ingredients - the chemistry model is the more interesting tool. Miso maps to savory, protein-rich, fermented things. That's a different list than what you'd find in a Japanese cookbook index.

Neither answer is wrong. They're answering different questions.

The Flavor Compass

The part of the paper I find genuinely surprising is the geography that emerges without anyone teaching it. Run the models and plot the results, and you get visible clusters - East Asian pantry, South Asian spice blends, Mexican and Latin American staples, Mediterranean savory ingredients - appearing on their own. Nobody labeled them. The models found them by learning which ingredients travel together.

The researchers call these "culinary modes." There are roughly 150 to 200 of them, depending on which model you use. Some are intuitive. Some are strange in a way that makes you want to cook something immediately.

The Direction Trick

The most playful part of the paper is something called direction arithmetic. You start with an ingredient and point it toward a cuisine. Then you dial a number - θ, from 0 to 60 degrees - that controls how far you travel.

At 0: you're still near the original ingredient. At 60: you've arrived in the target cuisine's pantry. Anywhere in between: you're in hybrid territory.

Rice pointed toward South Asia retrieves curry leaf, dal, fenugreek, Kashmiri chili as θ increases. Corn pointed toward Latin America surfaces tomatillo, queso fresco, salsa, corn tortilla. It's less a recommendation engine and more a flavor compass - the kind of tool that could help a cook understand what makes a cuisine coherent from the inside.

What It Can't Do (Yet)

The recipe data skews heavily English and Chinese. Smaller culinary traditions - West African, Central Asian, indigenous American - have thinner coverage, which means the map gets less reliable in those regions. The chemical data only covers about a third of the full ingredient list. And the researchers haven't released the trained models yet, so you can't go use this today.

But the framing matters independent of the implementation. Food isn't one-dimensional. "Similar" isn't one thing. The tools we build for cooks - recipe search, substitution suggestions, pairing recommendations - mostly pretend otherwise. This paper doesn't.