Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
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A Recommender System Using Factorization Machine

Factorization Machine을 이용한 추천 시스템 설계

  • Jeong, Seung-Yoon (Division of Information Security Graduate School of Information Security, Korea University) ;
  • Kim, Hyoung Joong (Division of Information Security Graduate School of Information Security, Korea University)
  • 정승윤 (고려대학교 정보보호대학원 빅데이터 응용 및 보안학과) ;
  • 김형중 (고려대학교 정보보호대학원 빅데이터 응용 및 보안학과)
  • Received : 2017.06.11
  • Accepted : 2017.07.28
  • Published : 2017.07.31
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Abstract

As the amount of data increases exponentially, the recommender system is attracting interest in various industries such as movies, books, and music, and is being studied. The recommendation system aims to propose an appropriate item to the user based on the user's past preference and click stream. Typical examples include Netflix's movie recommendation system and Amazon's book recommendation system. Previous studies can be categorized into three types: collaborative filtering, content-based recommendation, and hybrid recommendation. However, existing recommendation systems have disadvantages such as sparsity, cold start, and scalability problems. To improve these shortcomings and to develop a more accurate recommendation system, we have designed a recommendation system as a factorization machine using actual online product purchase data.

데이터의 양이 기하급수적으로 증가함에 따라 추천 시스템(recommender system)은 영화, 도서, 음악 등 다양한 산업에서 관심을 받고 있고 연구 대상이 되고 있다. 추천시스템은 사용자들의 과거 선호도 및 클릭스트림(click stream)을 바탕으로 사용자에게 적절한 아이템을 제안하는 것을 목적으로 한다. 대표적인 예로 넷플릭스의 영화 추천 시스템, 아마존의 도서 추천 시스템 등이 있다. 기존의 선행 연구는 협업적 여과, 내용 기반 추천, 혼합 방식의 3가지 방식으로 크게 분류할 수 있다. 하지만 기존의 추천 시스템은 희소성(sparsity), 콜드스타트(cold start), 확장성(scalability) 문제 등의 단점들이 있다. 이러한 단점들을 개선하고 보다 정확도가 높은 추천 시스템을 개발하기 위해 실제 온라인 기업의 상품구매 데이터를 이용해 factorization machine으로 추천시스템을 설계했다.

Keywords

References

  1. P. Paatero and U. Tapper, "Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values," Environmetrics, vol. 5, no. 2, pp. 111-126, 1994. https://doi.org/10.1002/env.3170050203
  2. S. Rendle, L. B. Marinho, A. Nanopoulos, and L. Schmidt-Thieme, "Learning optimal ranking with tensor factorization for tag recommendation," in Proceeding of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 727-736, 2009.
  3. S. Rendle and L. Schmidt-Thieme, "Pairwise interaction tensor factorization for personalized tag recommendation," in Proceedings of the ACM International Conference on Web Search and Data Mining, pp. 81-90, 2010,
  4. J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," in Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp. 43-52, 1998.
  5. R. J. Mooney and L. Roy, "Content-based book recommendation using learning for text categorization," in Proceedings of the ACM Conference on Digital Libraries, pp. 195-204, 2000.
  6. S. Banerjee and A. Roy, Linear Algebra and Matrix Analysis for Statistics, Chapman and Hall/CRC, 2014.
  7. Y. Koren, R. Bell, and C. Volinssky, Matrix factorization technique for recommender filtering, IEEE Computer, vol. 42, no. 8, pp. 30-37, 2009.
  8. R. H. Keshavan and S. Oh, "A gradient descent algorithm on the grassman manifold for matrix completion." arXiv preprint arXiv:0910.5260, 2009.
  9. S. Rendle, "Factorization machines," in Proceedings of the IEEE International Conference on Data Mining, pp. 995-1000, 2010.
  10. K. Madsen, H. B. Nielsen, and O. Tingleft, Methods for Non-Linear Least Squares Problems, Lecture Note, Informatics and Mathematical Modelling, Technical University of Denmark, 2004.
  11. S. K. Trivedi, Probability and Statistics with Reliability, Queueing, and Computer Science Applications, John Wiley & Sons, 2002.
  12. K. Mohan and J. Pearl, "On the testability of models with missing data," in Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 643-650, 2014.
  13. I.-H. Jung, "The phase space analysis of 3D vector fields," Journal of Digital Contents Society, vol. 16, no. 6, pp. 909-916, 2015. https://doi.org/10.9728/dcs.2015.16.6.909
  14. T. Hastie, S. Rosset, R.Tibshirani, and J. Zhu. "The entire regularization path for the support vector machine," Journal of Machine Learning Research. vol. 5, pp. 1391-1415, 2004.