Tourist attraction route intelligent recommendation method based on a mobile internet and big data analysis

Abstract

The invention discloses a tourist attraction route intelligent recommendation method based on a mobile internet and big data analysis, and the method comprises the steps: screening out candidate recommendation scenic spots from all scenic spots through the real-time positioning of the geographic positions of tourists, and screening out a target recommendation scenic spot through integrating the distance of all candidate recommendation scenic spots, the real-time number of tourists and the tourist attraction visiting popularity, recommending to tourists and navigating touring routes, thus intelligent recommendation and touring route navigation of the current touring scenic spots of the tourists are realized, and compared with a traditional manual scenic spot screening recommendation method, the recommendation method is high in intelligent level and recommendation efficiency, the recommendation basis is expanded, the recommendation function is perfected, According to the method, the screened target recommendation scenic spot can meet the distance proximity recommendation principle, and the off-peak sightseeing and the sightseeing popularity of the scenic spot can be considered, so that the recommendation effect is improved, the sightseeing experience of the tourist is enhanced, and the rapid and comprehensive recommendation requirement of the tourist for sightseeing the scenic spot in real time is greatly met.

Description

technical field [0001] The invention belongs to the technical field of tourist attraction route recommendation, in particular to an intelligent recommendation method for tourist attraction routes based on mobile Internet and big data analysis. Background technique [0002] With the increase of residents' income, people's consumption concept has also changed. Sightseeing and vacation travel in leisure time have become a fashion consumption for people. At the same time, with the emerging technologies of self-driving travel, self-guided travel, independent travel and other emerging tourism forms, more and more people choose to travel freely instead of following Tour group travels. For some large-scale tourist attractions, most of them contain multiple tourist attractions. When tourists enter the tourist attraction, they need to filter and recommend the current attractions. Screening recommendation, its screening recommendation method is low in intelligence level, and the scre...

AI Technical Summary

Problems solved by technology

For some large-scale tourist attractions, most of them contain multiple tourist attractions. When tourists enter the tourist attraction, they need to filter and recommend the current attractions. Screening recommendation, its screening recommendation method is low in intelligence level, and the screening efficiency is low. On the one hand, the screening basis is only based on the distance to perform nearby screening. Specifically, it is to screen the scenic spots closest to the tourist's geographical location, without considering the real-time location of the scenic spots. The influence of the number of tourists and the popularity of scenic spots on the selection of scenic spots. When there are too many real-time tourists in the scenic spot closest to the tourist's geographical location, it is often difficult to have a good tour experience when visiting the scenic spot at this time; on the other hand, for For some tourists who are crazy about roads, it is difficult to find the correct tour route according to the tour route map of the scenic spot, which leads to slow progress of the tour and wastes a lot of time searching for the route

[0003] To sum up, it can be seen that the traditional scenic spot selection and recommendation method has a single recommendation basis and imperfect recommendation function, which leads to poor recommendation effect, reduces tourists' sense of tour experience, and cannot meet the rapid and comprehensive recommendation needs of tourists for real-time sightseeing spots.

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