A Machine Learning Model for Recommending Restaurants based on User Ratings

However, oftentimes people just search a restaurant by using word “restaurant”, while the word “restaurant” means differently to different individuals. For an Asian, it can mean a “Chinese restaurant” or “Thai restaurant”. How to correctly interpret search requests based on people’s preference is a challenge. Building a machine-learning model based on activity history of a registered user can solve this problem. The activity histories used by this research are reviews and ratings from users. This project introduces a data processing pipeline, which uses reviews from registered users to generate a machine-learning model for each registered user. This project also defines an architecture, which uses the generated machine-learning models to support real-time personalized recommendations for restaurant searching and type of foods good at those recommended restaurants. Finally, this project aims to develop a good machine learning model, different collaborative filtering methodologies are considered to predict restaurants using user ratings. Slope One, k-Nearest Neighbors algorithm and multiclass SVM classification are some of the collaborating methodologies are going to consider in this