RESTAURANT RECOMMENDATION SYSTEM USING SOCIAL MEDIA ANALYSIS

Abstract

Nowadays number of people using mobile applications for food and hotel searching constantly increasing, but to recommend right hotel or dish for each user according to his/her personal interest is difficult task. In this project an intelligent recommendation system is proposed for more personalized experience. This project considers customer’s interest along with previous ratings for that particular hotel and food item. The substantial growth of the social web poses both challenges and opportunities for research in RSs. The main reason for this is the fact that the social web transforms information consumers into active contributors, allowing them to share their status, comment or rate web content. Restaurant Recommender is an user-based recommendation website that recommends restaurant to the users according to their preferences but change in person’s taste vary from time to time and that’s why we are going to use user-based collaborative filtering algorithm in this system which provides a restaurant recommendation according to the user’s interests and their past data which will consequently predict their future interests. Some of the data will be predefined on which basis the ratings of each restaurant will be stored and the new users can learn about them by seeing their ratings and can pick any ofthem based on their preference and then give their own reviews after dining there.