Mood Based Food Recommendation System

The constantly expanding volume of online information is a strategic approach to managing the excessive amount of data. The importance of recommender systems cannot be emphasized, considering their ubiquitous use in many online applications and their ability to alleviate many problems associated with over-choice. All potential parts of enterprise are the rising usage of technology that demands usage of IT. The hotel and restaurant industry nowadays are one of the most expanding businesses and has greatly contributed to the economy of the nation. Existing restaurant recommendation does not consider user's current point of view or is not personalized. The proposed system is completely personalized for users, this system recommends food and available restaurants based on user's current mood. The dataset from Zomato is taken to locate the restaurants based on location of user. A website is designed where the user must enter their basic personal details for developing their personalized system, and then select their current mood out of the options provided. Based on given inputs the application recommends user with food items and restaurant. Multiple options are provided to the user along with restaurant rating to give better experience. Total 9 restaurants are recommended to a customer out of which top-3 are the best recommendations and 6 are other recommendations. This model is developed using PyCharm, the restaurants are grouped by location using KNN algorithm. Flask is used to create website which is user friendly. This application can be used when a customer doesn't understand what to eat when they are in the moods.