A Scalable Recommendation based Approach for Predictive Restaurant Ratings and Personalized Content Filtering using Zomato Data

In recent years, the exponential growth of online restaurant platforms has revolutionized the dining experience, enabling users to explore various culinary options. Consequently, restaurant rating prediction and personalized recommendation systems have become indispensable tools for users seeking dining suggestions and platform operators aiming to enhance user engagement and satisfaction. The proposed integrated framework for Zomato combines rating prediction with content filtering to optimize user experiences. By integrating machine learning models, the ratings are predicted based on features like location and cuisine type, while content filtering personalizes recommendations. Real-world Zomato data experiments validate our framework’s efficacy in enhancing user satisfaction and engagement, offering tailored suggestions aligned with individual preferences. This work has an accuracy of 0.89 and f1 score of ${0. 8 1}$. Experimental results show significant improvements in rating prediction accuracy and the effectiveness of personalized recommendations, highlighting the potential of our approach to enhance the overall user experience on Zomato.