Developing a Cloud-Based Restaurant Recommendation System Using User Experience

Abstract

By giving individualized eating alternatives based on user preferences and contextual data, a cloud-based restaurant recommendation system employing Term Frequency - Inverse Document Frequency (TF-IDF) promises to improve customer happiness. The TF-IDF algorithm is used to analyze user evaluations, restaurant attributes, and eating patterns to find crucial decision-making elements. This method effectively analyzes massive datasets on a scalable cloud platform to provide real-time, reliable suggestions. Designing of an intelligent system that adjusts to user preferences, enhances decision-making, and builds loyalty via a customized eating experience is studied. Combining natural language processing with TF-IDF improves restaurant classification and ranking by extracting useful insights from textual reviews. This method provides scalability, enabling the system to serve varied populations and changing culinary trends, enhancing eating ecosystem engagement and pleasure.