FoodBar is a web app that mines user data to create personalized restaurant recommendations, like how Netflix combines your viewing habits with those of others with similar interests to give you movie recommendations.

The idea came last month when a woman from England told one of us that it was her first night in Waterloo, and that she didn't know where to have dinner. We asked her what types of food she likes, and we gave her a recommendation. The next day, we found out that we had dissimilar tastes and that she didn't like the restaurant that we had picked for her. That's where we had the idea of creating a platform that allows the traveller and curious foodie to discover restaurants that they will like, no matter where they are in the world.

Using the Yelp API as a primary datasource combined with a list of user preferences and other user reviews, FoodBar creates a social graph that creates clusters of people with similar dining preferences from which we can extrapolate individualized restaurant suggestions.

We've constantly found ourselves in this situation even at home in Waterloo: we don't know where the best place to buy shawarmas is, or where the best place to go on a date might be. What sets us apart from apps such as Urbanspoon is that our data isn't based on the average of all reviews, it's based on what people just like you have to say.

Share this project: