UCLA Anderson MBA students conduct Applied Management Research field study projects in lieu of a thesis. Anderson gives full-time students the Business Creation Option in which entrepreneurs launch companies while still in school. Team Connoisseur researched the restaurant landscape in L.A. to devise a delicious formula for picking local dining options that improves on standard rating sites.
By Zodin Del Rosario (’17), Richard Fernandez (’17), Kimberly Kalb (’17), Christina Rath (’17) and Sean Ginley (’17)
The restaurant search process is broken. With so many restaurants in L.A., people feel overwhelmed, and current websites like Yelp prove untrustworthy and cumbersome for people to sort through.
Connoisseur is a mobile app designed to offer personalized restaurant recommendations based on your dining history.
To gather seed data for our algorithm, the Connoisseur team surveyed 82 respondents to rate all the restaurants they had visited in Santa Monica. We explored multiple methodologies for predicting restaurant scores, including a k-nearest neighbors algorithm (k-NN), in which users are plotted in vector space and the actual physical distance between users is measured. In a k-NN, users with similar restaurant ratings will be closer to each other. We looked to find the five nearest neighbors to any user. Taking the average ratings of these five nearest neighbors, we found the other restaurants that these similar users loved.
We were able to test the viability of this approach by removing some of the data and using the algorithm to predict the deleted scores. This method was most effective when users had rated at least 25 restaurants, meaning once we moved outside of Santa Monica we would need users to rate even more in order to give accurate predictions.