원문정보
초록
영어
Numerous tourist-related data produced on the Internet contain not only simple tourist information but also diverse ideas and opinions from users. In order to derive meaningful information about tourist sites from such big data, the social network analysis of tourist keywords can identify the frequency of keywords and the relationship between keywords. Thus, it is possible to make recommendations more suitable for users by utilizing the clear recommendation criteria of tourist attractions and the relationship between tourist attractions. In this paper, a recommendation system was designed based on tourist site information through big data social network analysis. Based on user personality information, the types of tourism suitable for users are classified through deep learning and the network analysis among tourist keywords is conducted to identify the relationship between tourist attractions belonging to the type of tourism. Tour information for related tourist attractions shown on SNS and blogs will be recommended through tagging.
목차
1. Introduction
2. Related Work
2.1 Big Data
2.2 Social Network Analysis
2.3 Definition of tourism type by personality type through deep learning
3. Design of Adaptive Recommendation System
3.1 System Overview and Configuration
3.2 Operation Process of Adaptive Recommendation System
4. Applications
4.1 Application of Toursim Recommended Systems
4.2 Social Network Analysis with tagging
5. Conclusion
References