정보과학회 논문지 (Journal of KIISE)

  • 제43권11호
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  • Pages.1188-1200
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  • 2016
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  • 2383-630X(pISSN)
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  • 2383-6296(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지
DOI QR코드

DOI QR Code

컨텐츠 유사도와 사회적 친화도 분석 기법을 혼합한 가치정보의 추천 시스템

Hybrid Recommendation System of Qualitative Information Based on Content Similarity and Social Affinity Analysis

  • 투고 : 2016.05.24
  • 심사 : 2016.08.20
  • 발행 : 2016.11.15
⟨ 이전 논문 다음 논문 ⟩

초록

추천 시스템은 개인에게 고도로 개인화된 아이템을 제공함으로써 아이템의 선택과 소비과정에서 발생하는 과부하를 줄여주고 효율성을 증대시키는 중요한 역할을 한다. 본 연구에서는 전통적인 추천 기법인 Content-Based(CB)기법과 최근 대두되는 Social Network-based(SN)기법을 접목하여 새로운 복합방식의 정보 추천 알고리즘을 제시한다. CB기법의 대표적인 한계점인 cold start problem과 SN기법에서 부족할 수 있는 추천 아이템의 전문성 문제를 상호 보완하는 형태가 되며, 특히 최근 소셜 네트워크의 특징인 비신뢰(non-trust) 기반의 영향력 있는 정보 확산자가 존재하는 환경에서 기법을 적용할 수 있도록 하였다. 또한 대부분 사람 추천 중심인 기존의 SN기법들과는 달리 사람에게 제공할 정보를 추천하는데 초점을 두며, 정보의 선정과정에서 개인의 소셜 네트워크와 실세계(real world)에서의 사회활동 정보를 모두 활용하여 더욱 더 개인화된 가치정보를 제공하고자 한다.

Recommendation systems play a significant role in providing personalized information to users, with enhanced satisfaction and reduced information overload. Since the mid-1990s, many studies have been conducted on recommendation systems, but few have examined the recommendations of information from people in the online social networking environment. In this paper, we present a hybrid recommendation method that combines both the traditional system of content-based techniques to improve specialization, and the recently developed system of social network-based techniques to best overcome a few limitations of the traditional techniques, such as the cold-start problem. By suggesting a state-of-the-art method, this research will help users in online social networks view more personalized information with less effort than before.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

  1. B. Bollobas, Modern graph theory, volume 184 of Graduate Texts in Mathematics, Springer-Verlag, New York, 1998.
  2. C. Smith. (2016, May 18). By the Numbers: 200+Amazing Facebook Statistics (April 2016) [Online]. Available: http://expandedramblings.co-m/index.php/by-the-numbers-17-amazing-facebook-stats/8/ (downloaded 2016, May 20)
  3. M. McGee, "EdgeRank is dead: Facebook's News Feed algorithm now has close to 100K weight factors," Marketing Land, 2013.
  4. F. Ricci, L. Rokach, and B. Shapira, Introduction to recommender systems handbook, pp. 1-35, Springer US, 2011.
  5. D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich, Recommender systems: an introduction, Cambridge University Press, 2010.
  6. I. King, MR. Lyu, and H. Ma, "Introduction to social recommendation," Proc. of the 19th international conference on World wide web, ACM, pp. 1355-1356, 2010.
  7. I. Guy, D. Carmel, "Social recommender systems," Proc. of the 20th international conference companion on World wide web, ACM, pp. 283-284, 2011.
  8. M. Balabanovic and Y. Shoham, "Fab: content-based, collaborative recommendation," Journal of Communications of the ACM, Vol. 40, No. 3, pp. 66-72, 1997.
  9. M. Pazzani and D. Billsus, "Learning and revising user profiles: The identification of interesting web sites," Journal of Machine learning, Vol. 27, No. 3, pp. 313-331, 1997. https://doi.org/10.1023/A:1007369909943
  10. G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions," Journal of Knowledge and Data Engineering, IEEE Transactions on, Vol. 17, No. 6, pp. 734-749, 2005. https://doi.org/10.1109/TKDE.2005.99
  11. X. Su and TM. Khoshgoftaar, "A survey of collaborative filtering techniques," Journal of Advances in artificial intelligence 2009, Vol. 4, 2009.
  12. Wikipedia. (2016, May 2). Collaborative filtering [Online]. Available: https://en.wikipe-dia.org/wiki/Collaborative_filtering (downloaded 2016, May 10)
  13. P. Resnick, N. lacovou, M. Suchak, P. Bergstrom, and J. Riedl, "GroupLens: an open architecture for collaborative filtering of netnews," Proc. of the 1994 ACM conference on Computer supported cooperative work, ACM, pp. 175-186, 1994.
  14. G. Chowdhury, Introduction to modern information retrieval, Facet publishing, 2010.
  15. J. Golbeck, Generating predictive movie recommendations from trust in social networks, Springer-Verlag, Berlin Heidelberg, 2006.
  16. P. Massa, P. Avesani. "Trust-aware recommender systems," Proc. of the 2007 ACM conference on Recommender systems, ACM, pp. 17-24, 2007.
  17. M. Jamali, M. Ester, "Trustwalker: a random walk model for combining trust-based and item-based recommendation," Proc. of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp. 397-406, 2009.
  18. J. Tang, X. HU, H. Gao, and H. Liu, "Exploiting Local and Global Social Context for Recommendation," Proc. Of the International Joint Conference on Artificial Intelligence, 2013.
  19. J. A. Golbeck, "Computing and applying trust in web-based social networks," DRUM, 2005.