Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Recommender Systems using SVD with Social Network Information

사회연결망정보를 고려하는 SVD 기반 추천시스템

  • 김민건 (유세스파트너스(주)) ;
  • 김경재 (동국대학교, 서울 경영대학 경영정보학과)
  • Received : 2016.12.05
  • Accepted : 2016.12.21
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Collaborative Filtering (CF) predicts the focal user's preference for particular item based on user's preference rating data and recommends items for the similar users by using them. It is a popular technique for the personalization in e-commerce to reduce information overload. However, it has some limitations including sparsity and scalability problems. In this paper, we use a method to integrate social network information into collaborative filtering in order to mitigate the sparsity and scalability problems which are major limitations of typical collaborative filtering and reflect the user's qualitative and emotional information in recommendation process. In this paper, we use a novel recommendation algorithm which is integrated with collaborative filtering by using Social SVD++ algorithm which considers social network information in SVD++, an extension algorithm that can reflect implicit information in singular value decomposition (SVD). In particular, this study will evaluate the performance of the model by reflecting the real-world user's social network information in the recommendation process.

협업필터링은 사용자의 선호도 평가자료를 이용하여 특정 사용자의 특정 상품에 대한 선호도를 예측하고 이를 이용하여 유사한 사용자에게 상품을 추천한다. 협업필터링은 전자상거래에서의 정보 과잉현상을 줄여 주기에 가장 인기 있는 개인화 기법이다. 그러나 협업필터링은 희소성과 확장성 문제 등을 가지고 있다. 본 연구에서는 희소성과 확장성 문제와 같은 협업필터링의 주요 한계점을 보완하고 추천과정에 사용자의 정성적이고 감성적인 정보를 반영하도록 하기 위하여 사회연결망 정보와 협업필터링을 접목하는 방안을 이용한다. 본 논문에서는 특이값 분해에 내재적인 정보를 반영할 수 있도록 확장한 SVD++에 사회연결망 정보를 고려할 수 있도록 한 Social SVD++ 알고리듬을 협업필터링에 접목한 새로운 추천 알고리듬을 이용한다. 특히, 본 연구는 추천과정에 실제 사용자의 사회연결망 정보를 반영하여 모형의 성과를 평가할 것이다.

Keywords

References

  1. Adomavicius, G. and A. Tuzhilin, "Toward the next generation of recommender systems : A survey of the state-of-the-art and possible extensions," IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6(2005), 734-749. https://doi.org/10.1109/TKDE.2005.99
  2. Billsus, D. and M. J. Pazzani, "Learning Collaborative Information Filters," In Proceedings of Recommender Systems Workshop. Tech. Report WS-98-08, AAAI Press, 1998, 46-54,
  3. Cho, Y. and J. Bang, "Social network analysis for new product recommendation," Journal of Intelligence and Information Systems, Vol.15, No.4(2009), 183-200.
  4. Debnath, S., M. Granguly, and P. Mitra, "Feature weighting based recommendation system using social network analysis", Proceeding of WWW 2008, 2008, 1041-1042.
  5. Deerwester, S., S. RT. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, "Indexing by Latent Semantic Analysis," Journal of the American Society for Information Science, Vol.41, 1990, 391-407. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
  6. Funakoshi, K. and T. Ohguri, "A Content-based collaborative recommender system with detailed use of evaluations," Proceedings of the 4th International conference on knowledge-based Intelligent Engineering systems and Allied Technologies, 2000, 253-256.
  7. Funk, S., "Netflix Update: Try This At home," http://sifter.org/-simon/journal/20061211.html, 2006
  8. Ghazanfar, M. A. and A. Pr"ugel-Bennett, "Leveraging Clustering Approaches to Solve the Gray-Sheep Users Problem in Recommender Systems," Expert Systems with Applications, Vol.41, No.7(2014), 3261-3275. https://doi.org/10.1016/j.eswa.2013.11.010
  9. Golbeck, J. and J. Hendler, "Filmtrust: Movie recommendations using trust in web-based social networks," Proceedings of the IEEE Consumer Communications and Networking Conference, 2006, 282-286.
  10. Gorrell, G. and S. Funk, "Generalized hebbian algorithm for incremental latent semantic analysis," Proceedings of Interspeech, 2006, 1325-1328.
  11. Jyun-Cheng, W., and C. Chui-Chen, "Recommending trusted online auction sellers using social network analysis", Expert Systems with Applications, Vol. 34, 2008, 1666-1679. https://doi.org/10.1016/j.eswa.2007.01.045
  12. Kang, B. -S., "A novel web recommendation method for new customers using structural holes in social networks," Journal of Industrial Economics and Business, Vol.23, No.5(2010), 2371-2385.
  13. Kang, B. -S., "Structural hole of social network analysis for recommending new items," Journal of the Korean Data Analysis Society, Vol. 13, No. 3(B), 1365-1377.
  14. Kim, K. and H. C. Ahn, "Hybrid recommender systems using cluster-indexing collaborative filtering and social network analysis", Proceedings of the 2010 Fall Conference of Korean Management Information Systems Society, 2010, 604-609.
  15. Kim, M. G. and K. Kim, "Recommender systems using structural hole and collaborative filtering," Journal of Intelligence and Intelligent Systems, Vol.20, No.4(2014), 107-120.
  16. Kim, Y. S., H. C. Lee, and H. J. Chun, "Development of hybrid recommender systems in e-commerce using SVD", Journal of Korea Society of Supply Chain Management, Vol.2, No.1(2002), 21-30.
  17. Koren, Y., "Factorization meets the neighborhood: a multifaceted Collaborative filtering model," Proceedings of the 14th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, 2008, 426-434.
  18. Koren, Y. and R. Bell, "Advances in collaborative filtering," Recommender Systems Handbook, Edited by Ricci, F., L. Rokach, B. Shapira, and P. B. Kantor, Springer US, 2011 145-186.
  19. Kumar, R., B. K. Verma, and S. S. Rastogi, "Social popularity based SVD++ recommender system," International Journal of Computer Applications, Vol.87, No.14(2014), 33-37. https://doi.org/10.5120/15279-4033
  20. Lee, H. D. and H. J. Kim, "Improving Collaborative Filtering with Rating Prediction Based on Taste Space", Journal of KIISE: Database, Vol.34, No.5(2007), 389-395.
  21. Liu, F. and H. J. Lee, "Use of social network information to enhance collaborative filtering performance," Expert Systems with Applications, Vol. 37, 2010 4772-4778. https://doi.org/10.1016/j.eswa.2009.12.061
  22. Park, J. H., Y. H. Cho, and J. K. Kim, "Social network: a novel approach to new customer recommendations", Journal of Intelligence and Intelligent Systems, Vol.15, No.1(2009), 123-140.
  23. Paterek, A., "Improving regularized singular value decomposition for collaborative Filtering," KDD-Cup and Workshop, ACM press, 2007, 39-42,
  24. Ricci, F., L. Rokach, B. Shapira, and P.B. Kantor, "Recommender Systems Handbook", Springer, 2011
  25. Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, "Application of dimensionality reduction in recommender systems-a case study," In ACM WebKDD Workshop, 2000
  26. Su, X. and T. M. Khoshgoftaar, "Collaborative Filtering for Multi-Class Data Using Bayesian Networks," International Journal on Artificial Intelligence Tools, Vol.17, No.1(2008), 71-85. https://doi.org/10.1142/S0218213008003789
  27. Wang, H. F. and C. T. Wu, "A Strategy-oriented Operation Module for Recommender Systems in E-commerce," Computers and Operations Research, Vol.39, No.10, 2012, 1837-1849. https://doi.org/10.1016/j.cor.2010.03.011
  28. Yang, W. and J. Dia, "Discovering cohesive subgroups from social networks for targeted advertising," Expert Systems with Applications, Vol. 34, 2008, 2029-2038. https://doi.org/10.1016/j.eswa.2007.02.028

Cited by

  1. 사회연결망 분석을 활용한 연관규칙 확장기법 vol.23, pp.4, 2016, https://doi.org/10.13088/jiis.2017.23.4.111
  2. 카테고리 연관 규칙 마이닝을 활용한 추천 정확도 향상 기법 vol.26, pp.2, 2016, https://doi.org/10.13088/jiis.2020.26.2.027
  3. 네트워크 중심성 척도가 추천 성능에 미치는 영향에 대한 연구 vol.27, pp.1, 2021, https://doi.org/10.13088/jiis.2021.27.1.023