Tourist Attraction Recommendation Method Based on Megadata and Artificial Intelligence Algorithm

Abstract

As China’s economy continues to grow of informational technology and mobile Internet industry, the online tourism industry has received more and more extensive attention and use. However, as an emerging industry, users often need to spend a lot of time to choose travel services that match their needs because of the complex amount of relevant information. Under such circumstances, this paper studied the recommendation method in travel platform. First, the big data is used to extract user data. Secondly, the current online travel business recommendation for users has the problem of low accuracy. The reason is that the services provided are still in traditional recommendation algorithm. In this paper, the Bayesian network is used to evaluate the user’s attribute preference and generate a data model, using effective methods in artificial intelligence algorithms to improve collaborative filtering algorithms and finally generate hybrid recommendation algorithms. Compared with the traditional recommendation method, the experimental results showed that the research can improve the recommendation accuracy of tourist attractions by 6.55%, increase the user’s satisfaction for the platform, and enhance the visit rate and retention rate of the tourist attraction recommendation platform.

Intelligent Geo-Tour Route Recommendation Algorithm Based on Feature Text Mining and Spatial Accessibility Model

“…4.6.6. Superiority of the Proposed Algorithm Through analysis, it can be concluded that the proposed algorithm has significant advantages over the methods used in the literature [5][6][7][8][9][10][11][12] and the commonly used electronic map methods used in tour route planning, as follows:…”

“…Yun et al [5] used the Naive Bayes model to evaluate users' interests. This method finally improved the collaborative filtering algorithm's performance and increased the recommendation accuracy.…”

“…Based on the analysis of existing studies, the proposed algorithm has significant advantages over the literature [5][6][7][8][9][10][11][12].…”

“…Reference [5] used the Naive Bayes method and collaborative filtering method to recommend scenic spots. The Naive Bayes classification algorithm itself has flaws.…”

Is information evaluated subjectively? Social media has changed the way users search for medical information

“…Furthermore, social media platforms further promote efficiency and matching, 10 thereby increasing user visits and retention rates on these platforms. 11 …”