Description of Invention
Annual rise in tourism increases demand for a robust tourism recommendation. Big data processing has been adopted as the main component of adaptive recommendation systems, which provide services that are designed to suit the needs of individual users. Big data processing can be applied to tourism recommendation to provide better options for tourists and reduce distance travelled by user in order to visit a location of their preference. Deep learning serves as a tool used to analyze big data for best outcome. In this system, Logistic Regression is used to categorize big data and recommend tourism types to suit the user's personality is devised based on sentiment analysis, distance and cost of visit. The system is scalable to handle the complexity of the data collected by the system. It informs the user locations with the lowest Mean Squared Error through cumulative values from different tourism criteria. The current best cumulative MSE achieved by the system is 9.482. Llama provides platform for natural language processing and feedback to user on the recommended places.