Intelligent Tourism Recommendation Algorithm based on Text Mining and MP Nerve Cell Model of Multivariate Transportation Modes

Currently, the recommendation method on tourist sight and tour route lacks of the mechanism of tourists’ interests mining and the precise tourist sights recommending, and the planned tour routes cannot properly and adequately combine with the real world environment. Meanwhile, the research on the multivariate transportation modes in tour route recommendation is not sufficient. Aim at the problems of the tourist sight and tour route recommendation in intelligent tourism recommendation system of the intelligent tourism construction, this paper brings forward a tourism recommendation algorithm based on text mining and MP nerve cell model of multivariate transportation modes. The research specially focuses on the optimal tourist sight matching algorithm based on tourists’ interests mining and the optimal tour route chain algorithm based on the multivariate transportation modes. First, it analyzes the problems on tourism recommendation, based on which, the tourist sight clustering algorithm on feature attribute label and the tourist sight text mining algorithm on interest label are developed. The mined tourist sights will approach tourists’ interests to the maximum extent. Secondly, Considering the critical impact of the selected transportation mode on motive benefit satisfaction in the tour route chain, the tour route chain algorithm based on the nerve cell model of multivariate transportation modes is developed. This algorithm combines with geographic information element and transportation element, and it simulates the bionic principle of input and output information process on MP nerve cell, then the tour route chain model based on the nerve cell of multivariate transportation modes is set up. Through the iteration of multiple layer nerve cell motive weight values and accommodation coefficients, the algorithm finally outputs the signal information flow motive values, in which the tour route chain with the maximum information flow motive value is generated. Thirdly, to testify the feasibility and practicalness of the algorithm, an experimental example in real-world environment is designed and performed. The feasible matched tourist sights and tour route chains are output, meanwhile, the three commonly used optimal route searching algorithms are set as the control group, and along with the developed algorithm, they are compared with each other on the aspect of optimal tour route chain. The experiment testifies that the developed algorithm is feasible and practical, and has advantages on the tourism recommendation. Through the algorithm design and the experiment, it finds that the mined tourist sights by the objective function in the algorithm can best match tourists’ interest labels. The algorithm adequately combines with the real world tourism data of the geographic information, traffic information and tourist sight information and outputs the tour routes that best match tourists’ interests. Compare with the control group algorithms, the tour routes output by the developed algorithm have the highest motive satisfaction, lowest time complexity and space complexity. The developed algorithm is mainly used as the embedded algorithm for the intelligent tourism recommendation system, whose direct aim is to provide service for the tourists. Meanwhile, it can also provide service for the tourism administrations to collect, manage and mine the interest data as well as discover knowledge, and help the government to optimize the urban transportation system, launch the public vehicles and optimize the transportation guarantee strategy.