Towards the Application of Artificial Intelligence: Cosine Similarity in Recommendation Systems Based on Collaborative Filtering

The development of digital tourism drives the need for a recommendation system in the tourism sector. The need for a recommendation system supported by the development of Artificial Intelligence allows the tourism industry to offer a highly customized experience to customers. One of them is analyzing behavioral data and customer interactions so that it can predict the most relevant products, services, or content to recommend. This study aims to develop a tourist destination recommendation model using the cosine similarity method. The Cosine Similarity method is easy to implement and fast to calculate. The dataset used in this study is obtained from 297 users and 57 place tourists. This study has contributed to applying scientific data in the tourism sector by utilizing online platform data to provide 3 cosimilarity-based tourism recommendations where the best model was obtained with an RMSE value at epoch 100 of 0.38. Further research is the use of more complex recommendation methods.