DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Electronics and Computer Science-IJAECS |
 Journal Home Volume Issue |
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Issue |
Volume-7,Issue-7 ( Jul, 2020 ) | |||||||||
Paper Title |
A Hybrid Recommendation System for Restaurants | |||||||||
Author Name |
Bhumika Narwani, Jatin Nawani, Shubham Kejriwal, Radha Shankarmani | |||||||||
Affilition |
Sardar Patel Institute of Technology, Mumbai, Maharashtra, India | |||||||||
Pages |
31-35 | |||||||||
Abstract |
Managing a restaurant can be a competitive and an arduous business. According to the Business Insider, as many as 60% of restaurants close within the 1st year of opening [1]. This competitive nature of the restaurant business makes it imperative for owners and managers to understand their customers at a more personal level. Predicting user preferences and recommending food items and restaurants are starting to become a popular advantage. Apart from driving up sales and creating a more personalised experience for the user, it drastically speeds up searches making it very convenient for the customer. In this project, we wish to propose a hybrid recommendation engine that bridges the gap between customers and restaurant owners by aiming to ameliorate user experience and increase restaurant sales. We have built a recommendation engine that enables restaurants to enhance foot-fall and give personalized recommendations to users by focusing on reviews, psychographic factors, lifestyle patterns and user location. By making use of methods like content-based filtering, collaborative filtering and using user’s Google check-in data to identify locations, we have created a hybrid recommendation system. We present a hybrid recommendation engine, that aims to alleviate problems present in each of the methods and create a fool-proof robust system. Keywords - Restaurant Recommendation, Hybrid System, Content-based Filtering, Collaborative Filtering, Location-based. | |||||||||
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