Xiang Nan

A personalized tourism recommendation system provides convenient and economically affordable travel information for individuals/groups. This recommendation system banks on accumulated and analyzed data for providing context-aware travel solutions. For improving the recommendation efficiency and data analysis of such systems, this article introduces a mining and filtering harmonized collaborative process, named as the collaborative mining and filtering process (CMFP), for reducing the data processing overheads and improving the recommendation ratio. In this process, the accumulated data from the global and personal travel, expenditure, and other information are collaboratively analyzed. This analysis is powered by knowledge-based transfer learning for reducing the retardation in the large data processing. Based on the context-based data analysis, the filtering and mining are jointly performed for providing recommendations. In the filtering process, the maximum processed contextual data are extracted for updating the current knowledge base. From this base, the recommendation for adaptable travel is recommended for the user. This process’s performance is analyzed using the metrics accuracy, data handling rate, mining time, and overhead.

With the rapid development of tourism and the Internet industry, tourism activities have increasingly become a fashion behavior of people. The role of intelligent tourism resources in tourism activities has gradually become prominent. In order to meet the needs of all kinds of users, the tourism management system services are developing in the direction of diversification and individualization, and recommending the tourism resource products that best meet the needs of users to users has become a top priority. This article aims to improve the practical value of the system through the intelligent functions of the tourism management system based on information security in the intelligent recommendation of tourism resources. The tourism management system can display the received information about tourists. Through the experimental research of the accompanying information security algorithm and the analysis of the recommendation of the tourism system, the intelligent functions of the tourism management system based on information security can be captured in the intelligent recommendation of tourism resources. Develop the tourism management system to solve efficiency problems and realize tourism management information. Experimental results show that based on information security, 80% of tourists have become a popular choice for smart recommendation countries, which will bring more convenience to tourists during the game.