Journal of Information Processing Systems

  • 제15권5호
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  • Pages.1141-1155
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  • 2019
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Personalized Movie Recommendation System Combining Data Mining with the k-Clique Method

  • 투고 : 2019.01.28
  • 심사 : 2019.08.25
  • 발행 : 2019.10.31
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초록

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities' detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.

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참고문헌

  1. W. H. Jeong, S. J. Kim, D. S. Park, and J. Kwak, "Performance improvement of a movie recommendation system based on personal propensity and secure collaborative filtering," Journal of Information Processing Systems, vol. 9, no. 1, pp. 157-172, 2013. https://doi.org/10.3745/JIPS.2013.9.1.157
  2. P. Viana, and J. P. Pinto, "A collaborative approach for semantic time-based movie annotation using gamification," Human-centric Computing and Information Sciences, vol. 7, article no. 13, 2017.
  3. D. Lee, "Personalizing information using user's online social networks: a case study of CiteULike," Journal of Information Processing Systems, vol. 11, no. 1, pp 1-21, 2015. https://doi.org/10.3745/JIPS.04.0014
  4. A. Souri, Sh. Hosseinpour, and A. M. Rahmani, "Personality classification based on profiles of social networks' users and the five-factor model of personality," Human-centric Computing and Information Sciences, vol. 8, article no. 24, 2018.
  5. F. Hao, D. S. Park, and Z. Pei, "When social computing meets soft computing: opportunities and insights," Human-centric Computing and Information Sciences, vol. 8, article no. 8, 2018.
  6. F. Hao, D. S. Sim, D. S. Park, and H. S. Seo, "Similarity evaluation between graphs: a formal concept analysis approach," Journal of Information Processing Systems, vol. 13, no. 5, pp. 1158-1167, 2017. https://doi.org/10.3745/JIPS.04.0048
  7. F. Hao, D. S. Park, G. Min, Y. S. Jeong, and J. H. Park, "k-cliques mining in dynamic social networks based on triadic formal concept analysis," Neurocomputing, vol. 209, pp. 57-66, 2016. https://doi.org/10.1016/j.neucom.2015.10.141
  8. F. Hao, D. S. Park, and Z. Pei, "Detecting bases of maximal cliques in social networks," in Proceedings of the 11th International Conference on Multimedia and Ubiquitous Engineering (MUE), Seoul, Korea, 2017.
  9. F. Ricci, L. Rokach, and B. Shapira, "Introduction to recommender systems handbook," in Recommender Systems Handbook. Boston, MA: Springer, 2011, pp. 1-35.
  10. H. Jafarkarimi, A. T. H. Sim, and R. Saadatdoost, "A naive recommendation model for large databases," International Journal of Information and Education Technology, vol. 2, no. 3, pp. 216-219, 2012.
  11. S. K. Solanki and J. T. Patel, "A survey on association rule mining," in Proceedings of the 5th International Conference on Advanced Computing & Communication Technologies, Haryana, India, 2015, pp. 212-216.
  12. A. S. Tewari and K. Priyanka, "Book recommendation system based on collaborative filtering and association rule mining for college students," in Proceedings of 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, India, 2014, pp. 135-138.
  13. C. W. K. Leung, S. C. F. Chan, and F. L. Chung, "Applying cross-level association rule mining to cold-start recommendations," in Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Workshops, Silicon Valley, CA, 2017, pp. 133-136.
  14. A. S. Tewari and A. G. Barman, "Collaborative book recommendation system using trust based social network and association rule mining," in Proceedings of the 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, India, 2016, pp. 85-88.
  15. P. Jomsri, "Book recommendation system for digital library based on user profiles by using association rule," in Proceedings of the 4th edition of the International Conference on the Innovative Computing Technology (INTECH), Luton, UK, 2014, pp. 130-134.
  16. G. D. Linden, B. R. Smith, and N, K. Zada, "Automated detection and exposure of behavior-based relationships between browsable items," U.S. Patent 9070156, 2015.
  17. N. Rubens, M. Elahi, M. Sugiyama, and D. Kaplan, "Active learning in recommender systems," in Recommender Systems Handbook. Boston, MA: Springer, 2015, pp. 809-846.
  18. P. Vilakone, D. S. Park, K. Xinchang, and F. Hao, "An efficient movie recommendation algorithm based on improved k-clique," Human-centric Computing and Information Sciences, vol. 8, article no. 38, 2018.
  19. F. Hao, G. Min, Z. Pei, D. S. Park, and L. T. Yang, "K-clique community detection in social networks based on formal concept analysis," IEEE Systems Journal, vol. 11, no. 1, pp. 250-259, 2015. https://doi.org/10.1109/JSYST.2015.2433294
  20. E. Gregori, L. Lenzini, and S. Mainardi, "Parallel k-clique community detection on large-scale networks," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 8, pp. 1651-1660, 2013. https://doi.org/10.1109/TPDS.2012.229
  21. G. Palla, I. Derenyi, I. Farkas, and T. Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society," Nature, vol. 435, no. 7043, pp. 814-818, 2015. https://doi.org/10.1038/nature03607
  22. J. M. Kumpula, M. Kivela, K. Kaski, and J. Saramaki, "Sequential algorithm for fast clique percolation," Physical Review E, vol. 78, no. 2, article no. 026109, 2008.
  23. F. M. Harper and J. A. Konstan, "The movielens datasets: history and context," ACM Transactions on Interactive Intelligent Systems, vol. 5, no. 4, article no. 19, 2016.
  24. C. Tofallis, "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, vol. 66, no. 8, pp. 1352-1362, 2015. https://doi.org/10.1057/jors.2014.103
  25. R. J. Hyndman and A. B. Koehler, "Another look at measures of forecast accuracy," International Journal of Forecasting, vol. 22, no. 4, pp. 679-688, 2006. https://doi.org/10.1016/j.ijforecast.2006.03.001
  26. S. Kim and H. Kim, "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, vol. 32, no. 3, pp. 669-679, 2016. https://doi.org/10.1016/j.ijforecast.2015.12.003