The rapid growth of the tourism industry, coupled with the increasing reliance on mobile technology, necessitates the development of intelligent applications that enhance user experience through personalized recommendations. This research investigates the application of machine learning techniques, specifically Collaborative Filtering and Content-Based Filtering, to create a mobile tourism application that addresses the challenges of providing relevant and tailored suggestions to users. The primary objective of this study is to improve user satisfaction and engagement by delivering precise recommendations based on individual preferences and behaviors. To achieve this, the research employs a mixed-methods approach, combining quantitative data analysis from user interactions and qualitative assessments of user feedback. The study utilizes datasets sourced from reputable platforms, including user ratings and demographic information, to train the recommendation algorithms effectively. The evaluation of the implemented system demonstrates a significant increase in recommendation accuracy, leading to enhanced user satisfaction and increased visitation rates to tourist attractions. The findings indicate that the integration of Collaborative Filtering and Content-Based Filtering not only optimizes the personalization of recommendations but also fosters user loyalty and engagement with the application. By leveraging machine learning techniques, tourism providers can better understand user preferences, ultimately leading to a competitive advantage in a crowded market. This study contributes to the ongoing discourse on the intersection of technology and tourism, offering valuable insights for future research and application development in this dynamic field.
