Abstract:
"In an era where dining out has become a quintessential social activity, selecting a restaurant that pleases everyone in a group can be a challenging endeavor. This project presents a novel Group-Based Restaurant Recommendation System that leverages user preferences and past dining experiences to provide personalized restaurant recommendations. The system is designed to cater to groups of friends and families, acknowledging the diverse culinary preferences that often exist within such gatherings.
Utilizing a rich and extensive dataset from Yelp, complemented by the Yelp API, our recommendation system employs a content-based filtering approach. This approach takes into account the unique characteristics and attributes of restaurants, as well as the individual dining history and cuisine preferences of each user. By analyzing and comparing these factors, the system delivers tailored restaurant recommendations that align with the collective tastes and past dining choices of the group, ensuring a delightful dining experience for all."
