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@article{197140,
        author = {N.Jeevana Deepa and Vasamsetti Mohana Durga and Akkala Sai Durga and Jagadam Chandana and Mattaparthi Mani Satya Venkatesh and Dr. Y. Venkat},
        title = {Restaurant Recommendation System Using User Preferences and Ratings},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5599-5603},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197140},
        abstract = {Choosing the right restaurant has become challenging due to the growing number of options available on online platforms. Many users spend a significant amount of time searching for places that match their taste and budget. This project focuses on developing a restaurant recommendation system that simplifies this process by providing personalized suggestions. The system considers user preferences such as cuisine, cost, and location along with ratings given by other users to generate relevant recommendations. By analyzing this information, it helps users discover restaurants that better suit their needs. The approach improves decision-making, saves time, and enhances the overall user experience. The system is designed to be simple, efficient, and adaptable for real-world applications.
The system also considers features like restaurant type, cuisine, location, and cost to make recommendations more accurate. By combining these factors, the model gives users more relevant and useful suggestions. We have also developed an online table booking system, so users can easily reserve tables at restaurants. This feature saves users time and effort.},
        keywords = {Restaurant Recommendation System, User Preferences, User Ratings, Personalized Recommendations, Collaborative Filtering, Content-Based Filtering, Machine Learning, Data Analysis},
        month = {April},
        }