Food Optimizer

Inspiration

We started with the love of AI. We wanted first to make an app that recommends the best workout with results and user happiness in mind. We realized that there were too many elements at play to be accurate with our recommendations.

What it does

So, we decided to focus on the taste of food. While focusing on the 6 senses, sweet, sour, salty, bitter, savory, and spicy, we can recommend the best recipe to suggest in our arsenal for the user. As the user reviews more and more of our recipes, we have a better idea of what the user likes.

How we built it

We built our program out of C++. Starting from the data representation, we worked our way towards data calculation, giving us an algorithm that gives the best recommended recipe based on previous reviews.

Challenges we ran into

With a lot of moving parts at play, our code was bug-prone. With enough trial and error, however, we were able to eliminate all of our bugs so that our code runs smoothly.

Accomplishments that we're proud of

With simple data, we can verifiably construct information that even the user themselves do not know. We are proud to use principles of Artificial Intelligence to model our algorithm to recommend the best recipe for our user.

What we learned

We learned that though under pressure, we could still make something rather incredible.

What's next for foodoptimizer

We could use a more accurate mathematical model to better reflect real life taste extremity and user preferences. We feel that with more development power and data, we could make a better algorithm that could recommend recipes more accurately to the user's favorites.

Michael Harris contributed the website portion of our project, but we were unable to add his email before 9 am.