Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

Social Network based Sensibility Design Recommendation using {User - Associative Design} Matrix

소셜 네트워크 기반의 {사용자 - 연관 디자인} 행렬을 이용한 감성 디자인 추천

  • Jung, Eun-Jin (Intelligent System Lab., Dept. of Computer Information Engineering, Sangji University) ;
  • Kim, Joo-Chang (Intelligent System Lab., Dept. of Computer Information Engineering, Sangji University) ;
  • Jung, Hoill (Intelligent System Lab., Dept. of Computer Information Engineering, Sangji University) ;
  • Chung, Kyungyong (School of Computer Information Engineering, Sangji University)
  • 정은진 (상지대학교 컴퓨터정보공학과 지능시스템연구실) ;
  • 김주창 (상지대학교 컴퓨터정보공학과 지능시스템연구실) ;
  • 정호일 (상지대학교 컴퓨터정보공학과 지능시스템연구실) ;
  • 정경용 (상지대학교 컴퓨터정보공학부)
  • Received : 2016.06.24
  • Accepted : 2016.08.20
  • Published : 2016.08.28
Download PDF
⟨ Previous Next ⟩

Abstract

The recommendation service is changing from client-server based internet service to social networking. Especially in recent years, it is serving recommendations with personalization to users through crowdsourcing and social networking. The social networking based systems can be classified depending on methods of providing recommendation services and purposes by using memory and model based collaborative filtering. In this study, we proposed the social network based sensibility design recommendation using associative user. The proposed method makes {user - associative design} matrix through the social network and recommends sensibility design using the memory based collaborative filtering. For the performance evaluation of the proposed method, recall and precision verification are conducted. F-measure based on recommendation of social networking is used for the verification of accuracy.

현대사회에서 추천 서비스는 클라이언트-서버 기반의 인터넷 서비스에서 소셜 네트워킹으로 변화되고 있다. 특히 최근에는 크라우드소싱과 소셜 네트워킹을 통하여 사용자에게 개인화 추천을 서비스하고 있다. 소셜 네트워크 기반 시스템은 메모리와 모델 기반 협력적 필터링을 이용한 추천 서비스 제공 방식과 목적에 따라 분류할 수 있다. 이에 본 논문에서는 소셜 네트워크 기반의 {사용자-연관 디자인} 행렬을 이용한 감성 디자인 추천을 제안한다. 제안하는 방법은 소셜 네트워크 기반에서 {사용자-연관 디자인} 행렬을 구성하고 메모리 기반 협력적 필터링을 이용하여 감성 디자인을 추천한다. 제안한 방법의 성능평가는 정확도와 재현율 검증을 진행한다. 정확도의 검증은 소셜 네트워크 기반의 추천 적용유무에 따른 F-measure를 사용한다.

Keywords

References

  1. S. Lee, S. Park, “A Quality Control Framework for High Quality Crowdsourcing,” Korean Institute of Information Scientists and Engineers, Vol. 40, No. 2, pp. 158-160, 2013.
  2. K. Jae, “Achievements and Challenges of Crowdsourcing Journalism,” Journal of Social Science, Vol. 25, No. 3, pp. 205-224, 2014.
  3. D. Lee, C. Lee, “A Study on the Applicability of Crowdsourcing for Cadastral Reform,” Korean Association of Cadastre Information, Vol. 28, No. 2, pp. 55-70, 2012.
  4. K. Chung, J. Kim, H. Jung, J. Lee, “An Item - based Collaborative Filtering Technique by Associative Relation Clustering in Personalized Recommender Systems,” Journal of KIISE : Software and Applications, Vol. 31, No. 4, pp. 387-536, 2004.
  5. H. Jung, H. Kim, K. Chung, M. Kim, W. Kim, K. Shin, D. Hong, S. Oh, "Sensibility Ergonomics Car Design Supporting Method using Information Filtering," The Korea Contents Society, pp. 5-6, International Convention Center Jeju, 2011.
  6. C. Jeong, “A Study on the Advertising Creative Based on the Technology Convergence,” Journal of the Korea Convergence Society, Vol. 6, No. 4, pp. 235-241, 2015. https://doi.org/10.15207/JKCS.2015.6.4.235
  7. J. Kim, J. Go, K. Lee, “A Scheme of Social Engineering Attacks and Countermeasures Using Big Data based Conversion Voice Phishing,” Journal of the Korea Convergence Society, Vol. 6, No. 1, pp. 85-91, 2015. https://doi.org/10.15207/JKCS.2015.6.1.085
  8. J. Park, J. Kim, "A study on identity and possibility of emotional design - Emotional design based on design science which overcomes the limitation of the instrumental point of view," Korean Society of Design Science, pp. 27-38, 2008.
  9. S. Chung, S. Lee, J. Um, S. Cho, “Developing knowledge creation framework based on crowdsourcing,” The Korean Institute of Industrial Engineers, Vol. 2012, No. 5, pp. 2521-2531, 2012.
  10. H. Jung, K. Chung, “P2P Context Awareness based Sensibility Design Recommendation using Color and Bio-signal Analysis,” Peer-to-Peer Networking and Applications, Vol. 9, No. 3, pp. 546-557, 2016. https://doi.org/10.1007/s12083-015-0398-z
  11. H. Jung, K. Chung, “Knowledge-based Dietary Nutrition Recommendation for Obese Management,” Information Technology and Management, Vol. 17, No. 1, pp. 29-42, 2016. https://doi.org/10.1007/s10799-015-0218-4
  12. S. H. Kim, K. Chung, “Emergency Situation Monitoring Service using Context Motion Tracking of Chronic Disease Patients,” Cluster Computing, Vol. 18, No. 2, pp. 747-759, 2015. https://doi.org/10.1007/s10586-015-0440-1
  13. K. Chung, R. C. Park, “P2P Cloud Network Services for IoT based Disaster Situations Information,” Peer-to-Peer Networking and Applications, Vol. 9, No. 3, pp. 566-577, 2016. https://doi.org/10.1007/s12083-015-0386-3
  14. H. Jung, K. Chung, “Discovery of Automotive Design Paradigm using Relevance Feedback,” Personals Ubiqutors Computing, Vol. 18, No. 6, pp. 1363-1372, 2014. https://doi.org/10.1007/s00779-013-0738-z
  15. Y. M. Jung, "Information Search theory," Inc. Gumi trading, 1993.