Restaurant Recommender System for VIT Students

When students go to a university, they tend to try different. Restaurants, even messes. When they order food, they crave a satisfaction, which is offered by the taste of food, amidst all the classes and assignments. If the food isn't satisfactory, they tend to get disappointed. We created a restaurant data mining system for the students of Vellore Institute of Technology, Vellore. It categorizes restaurants into three categories: Inside VIT, Outside VIT, Messes. It displays the menu of each restaurant along with their categories as vegetarian and non vegetarian. It also contains the price and rating of the food item. The user can view, update, insert or delete restaurants and its corresponding food items. The system displays the top 2 recommended foods from both veg and non - veg, which are refreshed in real - time upon updating, insertion or deletion of food items. The recommendations are made by mining through the data using hash - maps, array lists and observable lists. The recommendations are made taking into account both the price and rating of a food item. A low - priced item, say tea, or soda, is dealt with separately.