Hybrid recommendation based on graph embedding

In recent years, online reservation systems of country hotel have become increasingly popular in rural areas. How to accurately recommend the houses of country hotel to the users is an urgent problem to be solved. Aiming at the problem of cold start and data sparseness in recommendation, a Hybrid Recommendation method based on Graph Embedding (HRGE) is proposed. First, three types of network are built, including user-user network based on user tag, house-house network based on house tag, and user-user network based on user behavior. Then, by using the method of graph embedding, three types of network are respectively embedded into low-dimensional vectors to obtain the characterization vectors of nodes. Finally, these characterization vectors are used to make a hybrid recommendation. The datasets in this paper are derived from the Country Hotel Reservation System in Guizhou Province. The experimental results show that, compared with traditional recommendation algorithms, the comprehensive evaluation index (F1) of the HRGE is improved by 20% and the Mean Average Precision (MAP) is increased by 11%.