Abstract
최근 다수의 소셜 네트워크가 빠르게 확산되었다. 그 중에서도 소셜 북마킹 시스템은 가장 널리 사용되는 것 중 하나이다. 소셜 북마킹 시스템은 사용자들이 온라인 자원에 태그를 부여해서 공유하고 관리할 수 있는 환경을 제공한다. 소셜 북마킹 시스템에서는 품질향상을 위해 태그와 시간 정보를 반영하여 개인에 특화된 추천을 할 수 있다. 본 논문에서는 가중치와 유사도 측정 과정에서 태그와 시간을 반영한 추천 시스템을 제안하였다. 또한 제안 방법론을 실제 데이터에 적용하였고, 실험결과 태그와 시간 정보를 함께 반영하였을 때 추천 성능이 향상됨을 확인하였다. In recent years, the number of social network system has grown rapidly. Among them, social bookmarking system(SBS) is one of the most popular systems. SBS provides network platform which users can share and manage various types of online resources by using tags. In SBS, it can be possible to reflect tag and time in order to enhance the quality of personalized recommendation. In this paper, we proposed recommender system which reflect tag and time at weight generation and similarity calculation. Also we adapted proposed method to real dataset and the result of experiment showed that the our method offers better performance when such information is integrated.
Sign-in/Register to access full text options 
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 callMore From: Journal of Korean Society for Internet Information
Disclaimer: 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.
