Travelling Route Recommendation Method Based on Graph Neural Network for Improving Travel Experience

With the rapid development of Internet technology, people can learn all kinds of travel information anytime and anywhere. However, the serious information overload causes travelers to be unable to make accurate and reasonable travel routes that meet tourists’ tastes for a while, thus reducing the quality of travel. The recommendation system as the mainstream solution to the information explosion of two means has received the attention of the majority of scholars and industry. Based on the research theory of tourist route recommendation, this paper analyzes the characteristics of attractions, factors affecting travelers’ travel experience when touring attractions and factors affecting travelers’ travel experience along tourist routes. Furthermore, we propose a tourist route recommendation model that meets tourists’ preferences. Then, this paper uses the graph neural network (GNN) algorithm to build a framework for tourist route recommendations based on the GNN using the relationship of preference and commonality existing among groups, tourists and attractions. The GNN algorithm is optimized and improved using multiple graphs and an attention mechanism. Finally, the effectiveness of this paper’s algorithm is verified by conducting experiments on different data sets.