Intelligent Personalized Tourism Recommendation Based on Optimized Unsupervised Multi Implicit Semantic Trajectory Mining Model

Abstract

With the advent of the big data era, tourists receive a massive amount of travel information every day, and traditional personalized travel recommendation methods are difficult to provide personalized travel recommendation services. Therefore, this study proposes an unsupervised multi cryptic semantic trajectory mining model and introduces a multi granularity recommendation framework based on attention mechanism to optimize it. Finally, an intelligent tourism personalized recommendation method based on the optimized unsupervised multi cryptic semantic trajectory mining model is constructed. The research results showed that compared with other algorithms, the recall and accuracy growth rates based on the optimized unsupervised multiple cryptic semantic trajectory mining model were the fastest, with corresponding recall and accuracy rates of 86.57% and 97.62%, respectively. In addition, compared with the most mainstream personalized tourism recommendation model based on improved frog leaping algorithm, the proposed model based on optimized unsupervised multiple implicit semantic trajectory mining model had an average increase of 10.3% and 8.9% in Hit Ratio and normalized cumulative loss gain. Finally, in practical applications, two tourism planning schemes for visiting Guilin were generated based on user needs, and users were very satisfied with the generated results. In summary, the method proposed in the study has good performance and can effectively improve user satisfaction and service quality in the tourism industry.