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.
