Abstract
Tourist attraction and tour route recommendation are the key research highlights in the field of smart tourism. Currently, the existing recommendation algorithms encounter certain problems when making decisions regarding tourist attractions and tour routes. This paper presents a smart tourism recommendation algorithm based on a cellular geospatial clustering and weighted collaborative filtering. The problems are analyzed and concluded, and then the research ideas and methods to solve the problems are introduced. Aimed at solving the problems, the tourist attraction recommendation model is set up based on a cellular geographic space generating model and a weighted collaborative filtering model. According to the matching degree between the tourists’ interest needs and tourist attraction feature attributes, a precise tourist attraction recommendation is obtained. In combination with the geospatial attributes of the tourist destination, the spatial adjacency clustering model based on the cellular space generating algorithm is set up, and then the weighted model is introduced for the collaborative filtering recommendation algorithm, which ensures that the recommendation result precisely matches the tourists’ needs. Providing precise results, the optimal tour route recommendation model based on the precise tourist attraction approach vector algorithm is set up. The approach vector algorithm is used to search the optimal route between two POIs under the condition of multivariate traffic modes to provide the tourists with the best motive benefits. To verify the feasibility and advantages of the algorithm, this paper designs a sample experiment and analyzes the resulting data to obtain the relevant conclusion.
Highlights
A smart recommendation system that provides suitable tourist attraction and route recommendations for tourists is a research highlight in the field of smart tourism [1]
It focuses on searching the very tourist attractions and optimal tour route that best match tourists’ interests, and this is the key aspect when setting up the algorithm process; in addition, its essence is different from that of other recommendation methods
The experiment chooses the city of Zhengzhou as an example and extracts the basic data of its tourist attractions, traffic information data, and geospatial data
Summary
A smart recommendation system that provides suitable tourist attraction and route recommendations for tourists is a research highlight in the field of smart tourism [1]. According to the association model among the tourist, tourist attraction and tourism label, the user interest model was set up to recommend tourist attractions. Ya Zhou [9] presented a kind of collaborative filtering recommendation method based on tourists’ location labels and preferences. Shi [11] designed a tourism collaborative filtering recommendation model that incorporates tourist attraction attributes. It used the clustering method DBSCAN to set up a user consumption model and obtain a the recommendation system for the WeChat applet. Chen [16] presented a tourism group recommendation method combined with collaborative filtering and user preferences. Shen [18] obtained massive historical tour data and sets up the personalized attraction similarity (PAS) model to recommend tourist attractions
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
