A Preference-Based Restaurant Recommendation System for Individuals and Groups. Team Size: 3

Using Yelp data, we built a restaurant recommendation system for individuals and groups. For each of 4.9k individual Yelp users, we create a ranking SVM model with features encompassing users’ food preferences and dietary restrictions, such as cuisine type, services o↵ered, ambience, noise level, average rating, etc. Our recommendation system for individual users achieved 72% average prediction accuracy. We propose a metric that maximizes minimum happiness across a group of users, as an alternative to other group recommendation systems where the most commonly recommended restaurant across individual users is selected.