The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Improved Internet Resource Recommendation Method using FOAF and SNA

FOAF와 SNA를 이용한 개선된 인터넷 자원 추천 방법

  • ;
  • 손종수 (고려대학교 전산학과) ;
  • 정인정 (고려대학교 컴퓨터정보학과)
  • Received : 2011.10.25
  • Accepted : 2012.01.27
  • Published : 2012.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In recent years, due to rapidly increasing user-created internet contents coupled with the development of community-based websites, the internet resource recommendation systems are attracting attentions of the users. However, most of the systems have failed in properly reflecting users' characteristics and thus they have difficulty in recommending appropriate resources to users. In this paper, we propose an internet resource recommendation method using FOAF and SNA which fully reflects the characteristics of users. In our method, 1) we extract the data about user characteristics and tags using FOAF; 2) we generate graphs representing users, user characteristics and tags after inserting data into 3 matrixes and integrating them; 3) we recommend the appropriate internet resources after selecting common characteristics of the recommended items and Hot tags by analyzing social network. For verification of our proposed method, we implemented our method to establish and analyze an experimental social group. We verified through our experiments that the more users added in the social network, the higher quality of recommendation result we got than the item-based recommendation method. By using the suggested idea in this paper, we can make a more appropriate recommendation of resources to users while effectively retrieving explosively increasing internet resources.

최근 사용자들이 생성한 콘텐츠들이 크게 늘어나고 커뮤니티 기반 웹 사이트가 발전함으로 인하여 사용자들에게 인터넷 자원을 추천하는 시스템이 큰 각광을 받고 있다. 그러나 대부분의 인터넷 자원 추천 시스템들은 사용자의 특징을 충분하게 반영하지 못하는 한계를 가지고 있다. 이에 따라 본 논문에서는 사용자의 특징이 충분히 반영되는 자원의 추천을 위하여 FOAF와 SNA를 사용한 추천 방법을 제안한다. 제안하는 방법은 1) FOAF를 통해 사용자의 특징 데이터와 태그 데이터를 취득한다. 2) 취득한 데이터를 세 종류의 행렬에 삽입하고 통합한 후 사용자, 사용자의 특징, 태그를 나타내는 그래프를 생성한다. 3) 소셜 네트워크 분석을 통해 추천 항목의 일반 특징과 핫태그(Hot tag)를 선정하여 인터넷 자원을 추천한다. 본 논문의 검증을 위하여 우리는 실험을 통해 본 논문에서 제안한 방법과 아이템 기반 추천 방법을 비교하였다. 이를 통해 보다 많은 사용자가 참여할수록 아이템 기반 추천 방법보다 본 논문에서 제안한 방법에 의한 추천 결과의 품질이 우수함을 확인하였다. 본 논문에서 제안하는 방법을 활용하면 사용자들에게 보다 적합한 자원을 추천하는 것이 가능하다. 그리고 제안하는 방법은 폭발적으로 늘어나는 인터넷 자원을 검색하는데 있어 효율적으로 활용될 수 있다.

Keywords

References

  1. L. Rainie, "The state of blogging.," Pew Internet &American Life Project, http://www.pewinternet.org/PPF/r/144/report_display.asp. 2005.
  2. Xin Li., Lei Guo., Yihong Zhao, "Tag-based Social Interest Discovery," International World Wide Web Conference Committee (IW3C2), ACM 978-1-60558-085-2/08/04, 2008.
  3. Anon Plangprasopchok., Kristina Lerman, "Exploiting social annotation for automatic resource discovery," AAAI workshop on Information Integration from the Web, 2007.
  4. http://en.wikipedia.org/wiki/FOAF_(software)#cite_note-0
  5. Maitrayee Mukherjee, Lawrence B. Holder, "Graph-based Data Mining on Social Networks," KDD'04, August 22-25, Seattle, WA, USA, 2004.
  6. Katarzyna Musiał1, Przemysław Kazienko,l and Tomasz Kajdanowicz, "Social Recommendations within the Multimedia Sharing Systems," WSKS 2008, LNAI 5288, pp.364-372, 2008.
  7. Amanda Lenhart, Kristen Purcell, Aaron Smith and Kathryn Zickuhr, "Social Media & Mobile Internet Use Among Teens and Young Adults," Pew Internet & American Life Project, 2010.
  8. Mukund Deshpande and George Karypis, "Item-Based Top-N Recommendation Algorithms," ACM Transactions on Information Systems, Vol.22, pp.143-177, 2004. https://doi.org/10.1145/963770.963776
  9. Jun Wang, Arjen P. de Vries and Marcel J.T. Reinders, "Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion," SIGIR'06, August 6-11, 2006, Seattle, Washington, USA, 2006.
  10. Ivan Cantador, Alejandro Bellogín and David Vallet, "Content-based Recommendation in Social Tagging Systems," Fourth ACM conference on Recommender systems Barcelona, Spain, 2010.
  11. Christian Desrosiers and George Karypis , "A Comprehensive Survey of Neighborhood-based Recommendation Methods," Springer Science & Business Media, LLC, 2011.
  12. Noor Ali-Hasan, Lada A Adamic, "Expressing social relationships on the blog through links and comments," Proc. of International Conference on Weblogs and Social Media, 2007.
  13. Michael F. Schwartz., David C. M. Wood, "Discovering shared interests using graph analysis," Communications of the ACM, 36(8):78-89, 1993. https://doi.org/10.1145/163381.163402
  14. Pu Wang and HongWu Ye, "A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering," International Conference on Industrial and Information Systems, 2009.
  15. Ahmed Hassan, Dragomir Radev, Junghoo Cho and Amruta Joshi, "Content Based Recommendation and Summarization in the Blogosphere," Third International ICWSM Conference, 2009.
  16. Gabor Takacs, István Pilászy, Bottyan Nemeth and Domonkos Tikk, "Scalable Collaborative Filtering Approaches for Large Recommender Systems ," Journal of Machine Learning Research 10, 623-656, 2009.
  17. L. M. de Campos, J. M. Fernandez-Luna, J. F. Huete and M. A. Rueda-Morales, "Using second-hand information in collaborative recommender systems ," Soft Compute, 2010.
  18. Buhwan Jeong, Jaewook Lee and Hyunbo Cho, "An iterative semi-explicit rating method for building collaborative recommender systems ," Expert Systems with Applications, Vol.36, Issue 3, Part 2, pp.6181-6186, 2009. https://doi.org/10.1016/j.eswa.2008.07.085
  19. Benjamin Heitmann and Conor Hayes, "Using Linked Data to Build Open, Collaborative Recommender Systems ," Association for the Advancement of Artificial Intelligence, 2010.
  20. Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete and Miguel A. Rueda-Morales, "Combining content-based and collaborative recommendations : A hybrid approach based on Bayesian networks ," International Journal of Approximate Reasoning Vol.51, Issue 7, pp.785-799, 2010.
  21. Toine Bogers, Antal Van Den Bosch, "Collaborative and Content-based Filtering for Item Recommendation on Social Bookmarking Websites ," ACM RecSys '09 Workshop on Recommender Systems and the Social Web, New York, NY, USA, 2009
  22. http://www.mybloglog.com/buzz/help/#a2005022821522712
  23. http://en.wikipedia.org/wiki/Delicious
  24. http://www.livejournal.com/site/about.bml
  25. Ae-Ttie Ji, Cheol Yeon, Heung-Nam Kim, and Geun-Sik Jo, "Collaborative Tagging in Recommender Systems ," AI 2007, LNAI 4830, pp.377-386, 2007.
  26. George Karypis, "Evaluation of Item-Based Top-N Recommendation Algorithms ," Proceedings of the tenth international conference on Information and knowledge management, 2001.
  27. Mohsen Jamali and Martin Ester, "A random walk model for combining trust-based and item-based recommendation ," 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009.
  28. Hoon-Ki Lee, Jung-Tae Kim, Jong-Hoon Lee and Eui-Hyun Paik, "Personalized Recommendation System for the Social Network Services Based on Psychographics," Fifth International Conference on Internet and Web Applications and Services, 2010.
  29. San-Yih Hwang, Chih-Ping Wei, Yu-chin Huang and Yun Tang, "Combining Coauthorship Network and Content for Literature Recommendation," Pacific Asia Conference on Information Systems, 2010.
  30. Jia Zhou, Tiejian Luo, "A novel approach to solve the sparsity problem in collaborative filtering ," Networking, Sensing and Control (ICNSC), 2010 International Conference, pp.165-170, 10-12 April, 2010.
  31. Irwin, M. D., and Hughes, H. L., "Centrality and the structure of urban interaction: measures, concepts, and applications," Social Forces, 17-51, 1992.
  32. Casciaro, T., "Seeing things clearly: social structure, personality, and accuracy in social network perception," Social Networks, 20, 331-351, 1998. https://doi.org/10.1016/S0378-8733(98)00008-2
  33. Scott, J, "Social Network Analysis ," Sage publications, Thousand Oaks, CA, 1991.