Tourism Enhancement Through Hybrid Mobile Application with Web Scraping and Recommendation System Using KNN and MF

Mobile applications have become an essential part of daily life. The tourists develop their tourist attractions and other facilities, which were being struggled after making plans within constraints. However, it is still incomplete and calls for some fine-tuning mechanisms with a view to creating the ideal tourist itinerary. This research has proposed a hybrid mobile app that integrates web scraping with machine learning technology to enhance the trip experience of visitors. The software does web scraping to scrape data in real time from several sites for bookings of hotels, flights, cabs, and food services with one interface. In solving the challenge of personalized tour planning, this app makes use of powerful machine learning techniques: K-Nearest Neighbors and matrix factorization-based recommendation algorithms. These algorithms consider user preferences for budget, length of travel, ratings, and other constraints while making personalized recommendations for tourism sites and activities. Software is constantly reacting to user feedback and behavior to enhance the relevance of recommendations over time. Unique in its method, it enables trip planning not only by unifying services but also makes sure travelers make informed and cost-effective decisions. It represents a huge step forward in intelligent, user-centric travel solutions by embedding the powers of web scraping for extensive data gathering and machine learning for personalization.