Have you ever wanted to try a new meal but don’t know what to get or where to go? Do you ever end up going to the same restaurants and get the same dishes because you don’t know how to find new meals you might like? Palette solves these problems in an easy-to-use app-based around recommending your meals based on your individual flavor palette! Through reviewing meals you’ve tried, Palette’s personalized recommendation algorithm will be able to find you not just any burger, but the perfect burger designed specifically based on what flavors you like.
I helped start Palette because I love trying new foods, but have a hard time expanding my tastes past the same dishes and restaurants I always go to. Modern food apps like Yelp and Google Reviews may recommend new restaurants to try, but these recommendations are based on others' preferences, not my own. Additionally these apps focus only on restaurants, not the actual dishes I'd be interested in eating——once I get to the restaurant, the best information these apps can provide me are the most popular dishes. This is why we created Palette: a mobile app that recommends dishes that are tailored to your taste. Whether it’s a taco truck or a Michelin starred restaurant, Palette will make sure you order your best meal, everytime.
Currently, the stakeholders of this project are the Dartmouth student population. If our product is met with success, we hope to integrate our concept more comprehensively with help of restaurants in the area. Looking to the future, we are also interested in expanding our service outside the Dartmouth community.
I was the Full-Stack Lead on this project and focused building out functionality/user-interaction, connecting pieces of our stack, and integrating new features/designs. Our team consisted of myself, 2 machine-learning developers, 1 back-end developer, and a PM/UI/UX lead. Click Here to view which pieces of the project I worked on.
Defining the problem ➜ Defining users ➜ Product research ➜ User research ➜ Sketches ➜ Grayscales ➜ Initial usability research ➜ Initial hi-fi designs ➜ Further usability research ➜ Final hi-fi designs ➜ Final round of usability research ➜ Prototyping
Deciding on stack (MERNG)➜ Setting Up Frontend/Backend ➜ Refining MVP goals ➜ Design ➜ Firebase Sign Up/Sign In Authentification ➜ MongoDB Setup ➜ Recommendations Functionality ➜ Liked Page Functionality ➜ Dish Page ➜ Reviews Functionality ➜ Ratings Functionality ➜ Profile Page ➜ Map Page ➜ Search Functionality ➜ Contact Restaurant Deeplinking ➜ Machine Learning Algorithm Development ➜ Building API for algorithm ➜ Styling
I would also like to thank my wonderful team (Kyle Bensink, Keen Morowitz, Connor Quigly, & Syed Tanveer) for their hard work, passion, and dedication to success. Finally, a huge thank you to Professor Joosten for his feedback and belief in our ability to achieve success.