A Personalized Attractions Recommendation Model based on Tourism Knowledge graph

Knowledge graph is a structured organization form of data, and it will be useful to introduce it into the tourism recommendation system as a kind of auxiliary information. The research proposes a model of attractions recommendation based on knowledge graph, which aims to use the semantic information and network structure of knowledge graph to encode the potential interests of tourists. Specifically, the model uses graph convolutional neural network to spread the embedding representation of attractions, and considers the importance of relationship and entity similarity in the convolution process to reflect the difference in preference of tourists. In addition, we also use the attention network to encode the sequential movement of tourists. The study developed a Beijing tourism knowledge graph to organize and share travel information, and used travel notes data to verify the model's performance. Experimental results show that the recommendation model based on tourism knowledge graph can effectively overcome the problem of data sparsity and achieve better performance than state-of-the-art models.