The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Movie Recommendation System based on Latent Factor Model

잠재요인 모델 기반 영화 추천 시스템

  • Ma, Chen ;
  • Kim, Kang-Chul (School of Electricity, Electronic communication and Computer Engineering, Chonnam National University)
  • ;
  • 김강철 (전남대학교 전기전자컴퓨터공학부)
  • Received : 2020.11.12
  • Accepted : 2021.02.17
  • Published : 2021.02.28
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Abstract

With the rapid development of the film industry, the number of films is significantly increasing and movie recommendation system can help user to predict the preferences of users based on their past behavior or feedback. This paper proposes a movie recommendation system based on the latent factor model with the adjustment of mean and bias in rating. Singular value decomposition is used to decompose the rating matrix and stochastic gradient descent is used to optimize the parameters for least-square loss function. And root mean square error is used to evaluate the performance of the proposed system. We implement the proposed system with Surprise package. The simulation results shows that root mean square error is 0.671 and the proposed system has good performance compared to other papers.

영화 산업의 빠른 발전으로 영화의 제작 수가 급격하게 증가하고 있으며, 영화 추천 시스템은 관객들의 과거 행동이나 영화 후기에 기반하여 관객들의 선호도를 예측하여 영화의 선택에 도움을 주고 있다. 본 논문은 평점의 평균과 편향의 보정을 이용하여 잠재요인 모델에 기반한 영화 추천 시스템을 제안한다. 특이값 분해 방법이 평점 매트릭스 분해에 사용되고, 통계 경사 하강법이 최소자승 손실 함수의 파라미터 최적합에 사용된다. 그리고 평균 제곱근 오차를 사용하여 제안한 시스템 성능을 평가한다. Surprise 패키지를 이용하여 제안한 시스템을 구현 하였으며, 모의실험 결과는 평균 제곱근 오차가 0.671이며, 다른 논문에서 방법에 비하여 좋은 성능을 가진다는 것을 확인하였다.

Keywords

References

  1. B. Patel, P. Desai, and U. Panchal, "Methods of recommender system: A review," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 2017, pp. 1-4.
  2. S. Sharma, A. Sharma, Y. Sharma, and M. Bhatia, "Recommender system using hybrid approach," 2016 International Conference on Computing, Communication and Automation (ICCCA), Noida, India, 2016, pp. 219-223.
  3. K. Shah, A. Salunke, S. Dongare, and K. Antala, "Recommender systems: An overview of different approaches to recommendations," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 2017, pp. 1-4.
  4. L. Chen and C. Kim, "Design of E-Commerce Service on The Web Based on Data Mining", J. of the Korea Institute of Electronic Communication Science, vol. 15, no. 04, Aug. 2020, pp. 703-708.
  5. S. Jain, A. Grover, P. Thakur, and S. Choudhary, "Trends, problems and solutions of recommender system," International Conference on Computing, Communication & Automation, Noida, India, 2015, pp. 955-958.
  6. W. Liu, B. Wang, and D. Wang, "Improved Latent Factor Model in Movie Recommendation System," 2018 International Conference on Intelligent Autonomous Systems (ICoIAS), Singapore, Singapore, 2018, pp. 101-104.
  7. R. Manjula and A. Chilambuchelvan, "Content Based Filtering Techniques in Recommendation System using user preferences," Int. J. Innov. Eng. Technol., vol. 7, no. 4, 2016, pp. 149-154.
  8. H. Ceong and C. Park, "Enzyme Metabolite Analysis Using Assoication Rules Mining", J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 10, 2016, pp. 969-982. https://doi.org/10.13067/JKIECS.2016.11.10.969
  9. S. Gong, H. Ye, and H. Tan, "Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System," 2009 Pacific-Asia Conference on Circuits, Communications and Systems, Chengdu, China, 2009, pp. 690-693.
  10. G. Linden, B. Smith, and J. York, "Amazon.com recommendations: item-to-item collaborative filtering," in IEEE Internet Computing, vol. 7, no. 1, 2003, pp. 76-80. https://doi.org/10.1109/MIC.2003.1167344
  11. S. Kim and D. Cho and S. Bracha, "Design and Implementation of Hashtag Recommendation System Based on Image Label Extraction using Deep Learning", J. of the Korea Institute of Electronic Communication Science, vol. 15, no. 04, Aug. 2020, pp. 709-716.
  12. Z. Jun-Yao, Z. Zi-Qian, S. Ji-Yun, and C. Jie-Hao, "Solutions to cold-start problems for latent factor models," 2017 17th International Symposium on Communications and Information Technologies (ISCIT), Cairns, Australia, 2017, pp. 1-5.
  13. Z. Zhang, Y. Xiao, W. Zhu, X. Jiao, K. Zhu and H. Deng, "A context-aware recommendation system based on latent factor model," 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, China, 2017, pp. 1-6.
  14. J. Zeng, "Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering", Master Thesis, The Ohio State University, 2017.
  15. H. Nguyen and T. Dinh, "A Modified Regularized Non-Negative Matrix Factorization for MovieLens," 2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future, Ho Chi Minh City, Vietnam, 2012, pp. 1-5.
  16. Y. Koren, "Factorization meets the neighborhood: a multifaceted collaborative filtering model," In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, U.S.A, 2008. pp. 426-434.
  17. M. Khoshneshin and W. N. Street, "Collaborative filtering via euclidean embedding", RecSys '10: Proceedings of the fourth ACM conference on Recommender systems, Barcelona Spain, Sep. 2010, pp. 87-94.