Improving the Accuracy of Tourism Recommendation System Based on Neural Collaborative Filtering

This study proposes a Neural Collaborative Filtering (NCF) model for tourism recommendation systems by integrating user ratings and review data. This model was developed to overcome the limitations of conventional recommendation systems that rely solely on numerical data, by adding contextual information from user reviews to improve the accuracy of preference prediction. The development process includes data preprocessing, conversion of text reviews into numerical representations using embedding techniques, and the application of NCF models with various parameter configurations. Experimental results show that the NCF model that combines rating and review data produces the best performance with Root mean Square Error (RMSE) values of 0.892, Hit Ratio at 10( HR@10) of 0.735, and Normalized Discounted Cumulative Gain at 10 (NDCG@10) of 0.629, outperforming models that only use one type of data. These results demonstrate that combining numerical and textual information can improve the model's understanding of user preferences, resulting in more relevant tourist destination recommendations. These findings contribute to the development of artificial intelligence-based recommendation systems in the tourism sector.