Fooddy is Your Food Buddy! Our aim is to bridge the gap between hungry people and their next dining getaway with minimal effort on the user’s side. Fooddy is targeted for individuals that are short on time or are not sure what to search for.

As people who dine out often, we found that it was not easy to decide on what to eat, even though we had a search engine like Yelp. We have tried many variations of food matching apps, and found that they are all their based on CUSTOMIZATION, and not by PERSONALIZATION. There were too many categories to choose from to filter out what we want to eat, as a refreshing change of cuisine on our recommendations list would be nice.

Frustrated by this problem, we decided to make an app to solve it!

What it does

Fooddy is an intelligent restaurant recommendation engine that learns the user's tastes and preferences as the user uses it. It extracts each restaurant's information and places it into a multi dimensional space, and uses the user's own taste preferences to find the best matches. However, it does not only rank base off of this information. It also takes into account how the user might want to try different cuisines every once in a while and ranks probabilistically.

Our model not only learns what the user likes, but it also takes into account what the user would get TIRED of seeing. It evolves with the user over TIME.

How we built it

We created this application on a web framework to allow users to use the application on any smart device.

Challenges we ran into

Some challenges were getting used to the API, web framework problems, and front end design, as none of us were familiar with UI/UX and front end technologies. This project served as a good way for us to explore the full stack. We also ran into a slight problem with efficiency of runtime, but as a hack and we were constantly busy with our lives, we figured it would be okay to let it pass for now.

Accomplishments that we're proud of

We are proud of our idea and how well our product works! We are proud of the decent results that our algorithm and model can give. We are proud that our peers and even professors found that our idea is interesting and are willing to use it as well. We are also proud of how much we achieved in such a short time, and the things we learned in doing so. We believe that with more fine-tuning, our product can become a full-fledge application, or be a core component of Yelp!

What we learned

We learned a lot of technologies and how to create a decent product. User experience and feedback is very important.

What's next for Fooddy

We plan to further develop and fine tune this application.

Thank you!

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posted an update

To run the application properly, PLEASE give the location to the application by pressing "Provide Location" on login/signup else it will not work. As it was a hack, core backend features were prioritized, and not all front end scenarios were resolved. Thank you

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