과제정보
이 논문은 2017년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2017S1A5A2A03067552)
참고문헌
- B. Vega-Marquez, C. Rubio-Escudero, J. C. Riquelme, and I. Nepomuceno-Chamorro, "Creation of synthetic data with conditional generative adversarial networks," International Workshop on Soft Computing Models in Industrial and Environmental Applications, (2019), 231~240.
- Chevalier, J. and Mayzlin, D, "The effect of word of mouth on sales: Online book reviews," J. of Marketing Research, 43(2006), 3.
- D. Billsus and M. . Pazzani, "Learning collaborative information filters," Proceedings of the Fifteenth International Conference on Machine Learning, Vol. 54(1998), 48.
- Douzas, G., & Bacao, F, "Effective data generation for imbalanced learning using conditional generative adversarial networks," Expert Systems with Applications, 91(2017), 464~471. https://doi.org/10.1016/j.eswa.2017.09.030
- Fiore, U., A. D. Santis, F. Perla, P. Zanetti, and F. Palmieri, "Using generative adversarial networks for improving classification effectiveness in credit card fraud detection," Information Sciences, 479(2017), 448~455. https://doi.org/10.1016/j.ins.2017.12.030
- G. Adomavicius, 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, 17(2005), 734~749. https://doi.org/10.1109/TKDE.2005.99
- Goldberg, D., Nichols, D., Oki, B. M., and Terry, D, "Using collaborative filterin g to weave an information tapestry," Communications of the ACM, 35(12) (1992), 61-70. https://doi.org/10.1145/138859.138867
- Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D.,Ozair, S., Courville, A., & Bengio., Y, "Generative Adversarial Nets," Advances in neural information processing systems, (2014), 2672~2680.
- H. Han, W. Y. Wang, and B. H. Mao, "Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning," Proceedings of the International Conference on Intelligent Computing, Berlin(2005), 878~887.
- H. He, Y. Bai, E. A. Garcia and S. Li, "ADASYN: Adaptive synthetic sampling approach for imbalanced learning," Proc. IEEE Int. Joint Conf. Neural Netw. IEEE World Congr. Comput. Intell.(2008), 1322~1328.
- H. M. Nguyen, E. W. Cooper, K. Kamei, "Borderline over-sampling for imbalanced data classification," International Journal of Knowledge Engineering and Soft Data Paradigms, 3(1)(2009), 4~21. https://doi.org/10.1504/IJKESDP.2011.039875
- Kim, Chae, Kim, "Generating and Visualizing Neighbors of Cold-start Users in Recommender System," Journal of the Korea Institute of Information and Communication Engineering, (2019), 134~135.
- Krawczyk B, "Learning from imbalanced data: open challenges and future directions," Progr. AI, 5 (4) (2016), 221~232.
- Kwon, Hong, "Method to Improve Data Sparsity Problem of Collaborative Filtering Using Latent Attribute Preference," Journal of Internet Computing and Services, Vol.14, No.5(2013), 59~67. https://doi.org/10.7472/JKSII.2013.14.5.59
- Lee Y, Kim SW, Park S, Xie X, "How to impute missing ratings? Claims, solution, and its application to collaborative filtering," In: Proceedings of the 2018 World Wide Web conference on World Wide Web(2018), 783~792.
- Lee, Kim, "Analysis of Data Imputation in Recommender Systems," Journal of KIISE, Vol. 44, No. 12(2017), 1333~1337. https://doi.org/10.5626/JOK.2017.44.12.1333
- Lu, L., Medo, M., Yeung, C. H., Zhang, Y. C., Zhang, Z. K., & Zhou, T, "Recommender systems," Physics Reports, 519(1) (2012), 1~49. https://doi.org/10.1016/j.physrep.2012.02.006
- M. K. Najafabadi, M. N. Mahrin, S. Chuprat, and H. M. Sarkan, "Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data," Comput. Hum. Behav, Vol. 67(2017), 113~128. https://doi.org/10.1016/j.chb.2016.11.010
- Michal Koziarski, Michal Wozniak, Bartosz Krawczyk, "Combined Cleaning and Resampling algorithm for multi-class imbalanced data with label noise," Knowl.-Based Syst, Vol 204(2020), 106223. https://doi.org/10.1016/j.knosys.2020.106223
- Mirza, M. and Osindero, S, "Conditional Generative Adversarial Nets," arXiv preprint arXiv:1411.1784, (2014).
- N. Hu, J. Zhang, and P. A. Pavlou. "Overcoming the J-shaped Distribution of Product Reviews," Communications of the ACM, Vol. 52, No. 10(2009), 144~147. https://doi.org/10.1145/1562764.1562800
- N. V. Chawla, et al, "SMOTE: synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, Vol.16(2002), 321~357. https://doi.org/10.1613/jair.953
- P. Melville, R. J. Mooney, and R. Nagarajan, "Content-Boosted Collaborative Filtering for Improved Recommendations," American Association for Artificial Intelligence (www.aaai.org) (2002), 187-192.
- R. Ghorbani and R. Ghousi, "Comparing different resampling methods in predicting Students' performance using machine learning techniques," IEEE Access, Vol. 8(2020), 67899~67911. https://doi.org/10.1109/access.2020.2986809
- R. Jin and J. Zhang, "Multi-class learning by smoothed boosting", Mach. Learn., Vol. 67, No. 3(2007), 207~227. https://doi.org/10.1007/s10994-007-5005-y
- Schoenmueller, V., Netzer, O., & Stahl, F, "The extreme distribution of online reviews: Prevalence, drivers and implications," Columbia Business School Research Paper(2018), No. 18~10.
- Seo, Jeon, Lee, Jung, Kim, "An Over-sampling Method based on Generative Adversarial Networks for Effective Classification of Imbalanced Big Data," Journal of the Korea Institute of Information and Communication Engineering(2017), 1030~1032.
- Shinhyun Ahn and Chung-Kon Shi, "Exploring Movie Recommendation System Using Cultural Metadata," Transactions on Edutainment II, Lecture Notes in Computer Science, Vol. 5660(2009), 119~134.
- Son, Kim, Kim, Cho, "Review and Analysis of Recommender Systems," Journal of the Korean Institute of Industrial Engineers, 41(2) (2015), 185~208. https://doi.org/10.7232/JKIIE.2015.41.2.185
- SongJie Gong, HongWu Ye, "An item based collaborative filtering using BP Neural Networks prediction," International Conference on Industrial and Information Systems(2009), 146~148.
- Z.-H. Zhou and X.-Y. Liu, "Training cost-sensitive neural networks with methods addressing the class imbalance problem," IEEE Trans. Knowl. Data Eng., Vol. 18, No. 1(2006), 63~77. https://doi.org/10.1109/TKDE.2006.17
- Zhang, C. X., Yang, M., Lv, J., & Wang, W. Q, "An improved hybrid collaborative filtering algorithm based on tags and time factor," Big Data Mining and Analytics, 1(2018), 48~56.