A Tourist Place Recommendation and Recognition System

Abstract

Tourism, these days involves mass availability and mass participation in holidays. But many times, a tourist cannot decide which place to visit, or where to stay. In this paper, we propose a mobile application, which will take the user's interest and recommend attractions, restaurants, and hotels. The system is trained using the dataset of TripAdvisor. The clustering of the training dataset is done using K-modes clustering which is an unsupervised learning algorithm. The application Travigate, not only recommends new places to the user, but it also helps them to recognize new places. With the use of Convolutional Neural Networks, reverse image search is done for a dataset created by web scraping images from Google. The application receives the data in the JSON format from the MySQL Database using Python Flask Server.