A Hybrid Approach to Restaurant Recommendation Using Geo-Location and User Reviews it will be the abundance of restaurant selections in the digital age makes it even more difficult to choose the best dining experience. In this work, a hybrid restaurant recommendation system that integrates user review analytics and geolocation data is presented. The suggested method provides context-aware, tailored suggestions by combining collaborative filtering based on sentiment analysis of user reviews with a content-based filtering technique that makes use of restaurant parameters like cuisine type, price, and location. Real-time relevance is ensured by dynamically modifying recommendations depending on a user's current location and travel restrictions using geolocation data. When tested on a variety of real-world datasets, the hybrid model outperforms conventional recommendation techniques in terms of accuracy, user satisfaction, and computing efficiency. By emphasizing the advantages of combining location data with qualitative user feedback for improved decision support in restaurant selection, this work advances the field of intelligent systems.
Javascript, Node.JS, Socket.IO, Mongo DB, Geolocation.
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