Earticle

초록

영어

In our previous work, a user similarity-based contents recommendation service using NFC was proposed for the same goal. This service used a small sample because it used only information of the user who had watched a museum. However, it has been shown that there are some limitations resulting from the difficulty of accurately predicting the user's preference. In order to lift this drawback, this paper introduces a user taste prediction service using big data for improving user-friendliness to a maximum. The proposed service predicts the user's taste using big data such as Twitter and blogs. It is possible to predict the exact user's preference and might recommend more suitable contents to the user's taste because it predicts the user`s taste using big data with a variety of user's social network information. So, it can recommend contents that match the user's taste. Our simulation results show the proposed big data-based approach can give each museum visiting user more accurate recommendation service appropriate to his or her taste compared with the previous one in terms of user preferences to exhibition-related contents.

목차

Abstract
 1. Introduction
 2. Related Work
 3. The User Taste Prediction Method
 4. Experimental System Architecture
 5. Performance Evaluation
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• YoonDeuk Seo [ Dept. of Comp. Scie., Kyonggi Univ., Iuidong, Yeongtong, Suwon 443-760 Gyeonggi, Republic of Korea ]
• Jinho Ahn [ Dept. of Comp. Scie., Kyonggi Univ., Iuidong, Yeongtong, Suwon 443-760 Gyeonggi, Republic of Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605