Research on the application of bilateral collaborative algorithm in hotel tourism consumption preference recommendation

Abstract

SummaryThe continuous expansion of the domestic tourism market has driven the development of the hotel related industry. It is a key focus of the hotel industry to provide accurate supporting services to tourists based on their preferences. However, the current recommendation model mainly considers tourist factors and lacks consideration for hotel factors. In order to address the concerns of recommendation services, a bilateral collaborative recommendation model is proposed, which first analyzes tourist feature data through comprehensive similarity calculation. Firstly, the comprehensive similarity calculation is used to analyze tourist feature data, and the K‐means Clustering Algorithm (K‐means) is introduced to improve the clustering effect between data. And to better distinguish tourist preferences, the Gale Shapley (G‐S) bilateral matching model is introduced to achieve preference matching of consumer features. In the Mean Absolute Error (MAE) analysis of the model, the User‐based Collaborative Filtering (UBCF) model performs the worst. When the k value is 90, the minimum MAE value is 0.7896. The Item‐based Collaborative Filtering (IBCF) model is 0.7776, with the best performance being the improved G‐S model of 0.7325. In the performance testing of three models' preference recommendations, the improved G‐S model achieves an accuracy of over 90% in all three types of feature data recommendations, with the lowest accuracy of 62.5% for UBCF and 71.3% for IBCF. It can be seen that the proposed improved G‐S model can accurately identify tourist and hotel preference data and accurately recommend corresponding projects. The research content provides important technical references for the digital transformation and development of the tourism industry.