Empowering Tourists with Context-Aware Recommendations using GAN

A technology-based solution known as a tourism recommendation system makes suggestions to visitors based on their preferences, prior travels, and experiences. These systems gather data from a variety of sources, including web searches, user reviews, travel history, etc. When there is a lack of data about some users or items, current technologies suffer from sparsity, which can lead to inaccurate recommendations. Another challenge that tourism faces is the diversity issue, which occurs when similarities are valued over preferences. GAN and context-aware recommendation systems are two prominent techniques that are combined in our system. The system's objective is to give personalized recommendations to travelers based on multiple contextual elements. The system creates new recommendations based on the knowledge it has gained by using GANs to identify patterns and connections between various elements of a tourist's setting and their preferences. Synthetic data is generated to supplement the original dataset, which can aid in overcoming the cold-start and sparsity issues. It can also aid in the development of more scalable recommendation systems.