Design And Application Of Tourism Service Recommendation Algorithm Based On Cloud Computing

"Wisdom tourism" is the main task of informatization construction of China’s tourism and is also hot topic in the research currently, its wisdom embodying three aspects:"tourism service wisdom","tourism management wisdom" and "tourism marketing wisdom". Tourism service recommendation is more highly of today’s tourism marketing strategy, and has been widely studied. To some extent, it is the embodiment of wisdom. Now tourism data is becoming increasingly large. Storage and processing of these data become a problem. At the same time, for information retrieval, search engines cannot meet the needs of user’s diversification and individuation, and also cannot effectively solve the problem of information overload. The recommendation engine as another method of information overload, can not only find a suitable or potential need information to users through the recommendation technology, but also can bring better experience for the user. But in the face of the challenge of the big data, a new platform and appropriate and effective algorithm is needed.As a good cloud computing platform, Hadoop is one of the best tools to research problem of big data. Its MapReduce can be used to calculate rapidly with huge amounts of data by distributed computing, and in terms of storage security, stability, and high fault tolerance, its HDFS is excellent. This paper tries to solve the problem of the mass travel data through coding algorithm on the Hadoop platform. The article makes the classical data mining algorithm FP-Grown to achieve parallelization, and realize the application of the recommendation in the travel service.Through the careful study on the recommendation algorithm and the system framework, this paper will focus on complete four modules:first, the big tourism data acquisition, which is prepared for all experiment; second, the tourism data analysis, in this paper the author makes analysis of tourism temporal and spatial statistical characteristics and center view of tourist flow center based social network; third, the algorithm design of the tourism service recommendation in cloud platform, the author study Hadoop platform and the classical FP-Growth algorithm deeply and carefully and realizes parallel FP-Growth algorithm, which is the core of commendation system; at last, completing the recommendation system and realizing tourism recommendation of some combinations on the six elements of tourism.In summary,studying personalized tourism information service under the background of big data and realizing the personalized service recommendation are a meaningful job. About the study of the tourism recommendation system based on Hadoop, on the one hand, the author study the algorithm model of tourism data cloud platform mining in theory, on the other hand exploring the tourism service under big data recommended system come true in practice.