Enhancing the Online Food Ordering Experience through Integrated Customer Reviews

The food delivery industry has witnessed a significant rise in recent years, driven by the growing demand for convenient and personalized dining experiences. As customers increasingly rely on online reviews to make informed decisions, leveraging this valuable feedback can be instrumental in enhancing the overall food ordering experience. This research paper presents a comprehensive framework for integrating customer reviews into food delivery platforms to provide personalized restaurant recommendations and improve user trust and satisfaction. By employing a hybrid recommendation approach that combines content-based and collaborative filtering techniques, the proposed system leverages review data, user preferences, and restaurant attributes to generate tailored suggestions. Furthermore, the study explores the implementation of explainable AI methods, such as LIME and SHAP, to enhance the transparency and interpretability of the recommendation models, thereby fostering user trust and ensuring accountable decision-making. The findings from this research offer valuable insights into the impact of review-driven recommendations on customer behavior and loyalty, while also contributing to the growing body of knowledge on user experience optimization in the food delivery industry.