TOURIST DESTINATION RECOMMENDATION SYSTEM

Abstract

Currently, suggestion systems are being widely applied in most fields (such as e-commerce, entertainment, education, tourism,...) to help users easily make their choices thanks to personal information or feedback (rating). The thesis focuses on researching a collaborative filtering recommendation system based on Keras's RecommenderNet model to predict the user's suitability for a tourist destination based on the user's rating history, and then suggest a list destination place to the user, and content-based recommended by vectorizing TF-IDF to convert feature place data into TF-IDF matrix and then use to calculate the cosine similarity between the vectors after that provide a list of a suggested destination place for the user. Finally, I apply this research to build a "tourist destination recommendation system" for users by combining collaborative filtering and content-based filtering, suggesting destinations for tourists, and integrating Vietmap to provide easy directions to tourist destinations.