Collaborative and Content-Based Filtering Hybrid Method on Tourism Recommender System to Promote Less Explored Areas

The COVID-19 pandemic has significantly impacted various areas of life, including tourism. Currently, the tourism sector is starting to recover and start its activities. However, several tourist attractions have not been explored, thus making visitors less aware of information about these tours. This affects the number of tourist visits. Therefore, there is a need of an information technology approach to promote tourism objects, including a tourist recommendation system. This study proposed a hybrid recommendation system incorporating collaborative and content based filtering. This model is proven to be able to produce good rating predictions on a recommendation system. This hybrid method uses a linear combination by calculating the rating matrix and user profile as the first step in providing rating predictions. Collaborative filtering is calculated using the cosine similarity algorithm and weighted sum algorithm, while the content-based filtering method is performed by calculating the weight of each available feature. We apply this model to the Palembang tourism dataset to the the website. This system recommends existing historical tourist attractions based on visitor criteria. The results show the existing data's effective, efficient, and accurate results. The calculation result that the rating prediction using the hybrid method is 3.203. In addition, this method can also help overcome existing cold start problems.