Paper Title
Personalized Restaurant Recommendation System based on Services

The recommendation system is now globally used in all types of fields, take it food, movie, or clothes recommendation. Nowadays everyone is ordering food online for which restaurant recommendation system is become essential to differentiate between multiple restaurants based on personal favourite cuisines. Whenever a person visits a new location, the user is stuck where to eat due to lack of information on taste and reviews. So in this paper, we contemplate the restaurant recommendation system based on users favourite restaurant, location, budget, and other preferences, which will recommend the restaurants to the user on the basis of amenities. In previous studies, collaborative, content-based and hybrid filtering was used for recommending restaurants but their recommendation system were unable to recommend the restaurants on the basis of user preference in different cities. In this paper, we propose a more personalized recommendation system by taking inputs from users as their favourite restaurant and recommending similar restaurants in different cities and preferred location by using machine learning algorithms, i.e., count vectorization and cosine similarity on the features of restaurant dataset to get the recommendation of restaurants. Keywords - Amenities, Content based Filtering, Cosine Similarity, Count Vectorization, Machine Learning, Recommendation System, User Preferences.