Abstract
Actual tourism mining models are often used to discover potential information in documents, but tourism models without human knowledge often produce unexplainable topics. This paper combines big data technology to build a personalized recommendation system for smart tourism, model the contextual information usage ontology under the tourism information system, and give the association between various ontologies. Then, this paper uses a matrix to describe each discrete attribute and interval attribute and uses a vector to model the user’s preferences. In addition, this paper constructs an intelligent recommendation system based on the actual needs of travel recommendation and verifies the system in combination with experimental research. Through experimental analysis, it can be known that the intelligent tourism personalized recommendation system based on big data technology proposed in this paper has a high practical effect.
Highlights
With the popularity and popularization of smart mobile devices, more and more people will use social networking sites to post photos, post videos, post comments, check in, etc., when traveling, to record their journeys, to facilitate memories, or to share with others. ese uploaded and shared contents all provide a wealth of resources for people to study the recommendation of tourist attractions. e user’s personal preferences are hidden in these resources uploaded by the user. rough analysis and statistics, it is possible to dig out the user’s preferences and the characteristics of the scenic spot itself, enrich the data source for user similarity calculation, and thereby provide the possibility to improve the recommendation effect of the collaborative filtering recommendation algorithm
At present, crawling and analyzing travel data uploaded by users from travel websites and researching personalized recommendations for tourist attractions has become a new research hotspot [1]
It can be seen from the above research that the intelligent tourism personalized recommendation system based on big data technology proposed in this paper has a high practical effect
Summary
Received 21 October 2021; Revised 4 November 2021; Accepted 18 November 2021; Published 17 December 2021. Actual tourism mining models are often used to discover potential information in documents, but tourism models without human knowledge often produce unexplainable topics. Is paper combines big data technology to build a personalized recommendation system for smart tourism, model the contextual information usage ontology under the tourism information system, and give the association between various ontologies. En, this paper uses a matrix to describe each discrete attribute and interval attribute and uses a vector to model the user’s preferences. This paper constructs an intelligent recommendation system based on the actual needs of travel recommendation and verifies the system in combination with experimental research. Rough experimental analysis, it can be known that the intelligent tourism personalized recommendation system based on big data technology proposed in this paper has a high practical effect This paper constructs an intelligent recommendation system based on the actual needs of travel recommendation and verifies the system in combination with experimental research. rough experimental analysis, it can be known that the intelligent tourism personalized recommendation system based on big data technology proposed in this paper has a high practical effect
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