원문정보
초록
영어
Adaptive recommendation systems have been developed with big data processing as a system that provides services tailored to users based on user information and usage patterns. Deep learning can be used in these adaptive recommendation systems to handle big data, providing more efficient user-friendly recommendation services. In this paper, we propose a system that uses deep learning to categorize and recommend tourism types to suit the user's personality. The system was divided into three layers according to its core role to increase efficiency and facilitate maintenance. Each layer consists of the Service Provisioning Layer that real users encounter, the Recommendation Service Layer, which provides recommended services based on user information entered, and the Adaptive Definition Layer, which learns the types of tourism suitable for personality types. The proposed system is highly scalable because it provides services using deep learning, and the adaptive recommendation system connects the user's personality type and tourism type to deliver the data to the user in a flexible manner.
목차
1. Introduction
2. Related Works
2.1 Deep Learning
2.2 Adaptive Recommendation System
2.3 Definition of personality type and tourism type
3. Design of Adaptive Recommendation System
3.1 System Overview and Configuration
3.2 Operation Process of Adaptive Recommendation System
4. Example of Applying
4.1 Learning of personality types and tourism types
4.2 Relationship between personality type and tourism type
5. Conclusion
References