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Abstract
With the deep cross-border integration of tourism and big data, the personalized demand of tourist groups is increasingly strong. Precision marketing has become a new marketing mode that the tourism industry needs to pay close attention to and explore. Based on the advantages of big data platform and location-based service, starting from the precise marketing demand of tourism, we design data flow mining technology framework for user’s mobile behavior trajectory based on location services in mobile e-commerce environment to get user track data that incorporates location information, consumption information, and social information. Data mining clustering technology is used to analyze the characteristics of users’ mobile behavior trajectories, and the precise recommendation system of tourism is constructed to provide support for tourism decision making. It can target the tourist group for precise marketing and make tourists travel smarter.
Highlights
IntroductionWith the continuous promotion of the strategic pace of building a well-off society in an all-round way, tourism has become an important part of the people’s daily life in China, marking that China’s tourism industry has entered the era of mass tourism
Location-based service (LBS) is a kind of value-added service provided by the combination of mobile communication network and satellite positioning system
As a new mobile computing service in recent years, 80% of the world’s information has time and location tags, and location services have developed to the big data stage [1]
Summary
With the continuous promotion of the strategic pace of building a well-off society in an all-round way, tourism has become an important part of the people’s daily life in China, marking that China’s tourism industry has entered the era of mass tourism Based on this background, in the mobile e-commerce environment, based on LBS location service, research and analysis of user’s mobile behavior trajectory can extract valuable user’s mobile behavior features from a large number of mixed dynamic data and integrate the mobile behavior and consumer behavior of tourism users. By analyzing the user’s characteristics through the trajectory of user’s mobile behavior, this paper constructs a travel recommendation system in the mobile point-to-point environment and a precise marketing model in the tourism industry based on the trajectory of user’s mobile behavior, so as to provide appropriate services for the appropriate users at the right time and place, in order to provide reference for relevant tourism enterprises to achieve precise marketing
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