Abstract
Tourism plays a major role in a country’s economy. But there is still a lack of a platform that provides personalized information regarding tourist attractions. If there exists a system that can provide personalized accurate information to tourists about local attractions, food, and shopping it will be a huge benefit for tourists. In this paper, we are proposing a hybrid approach of recommended systems to recommend tourist attractions for users. This recommendation process involves a combination of both content and collaborative filtering approach. This Hybrid approach avoids the disadvantages in both the methods and provides users with accurate information. To calculate the similarity between items the cosine similarity method is adopted. We have applied a model-based collaborative filtering approach called SVD for better results. The weighted hybridization approach is used to combine the results of both methods. The data of tourist attractions and users have been collected for implementation. This approach has given better results compared to CB and CF filtering methods separately.
