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[3]. Deng, L. & Yu, D. Deep Learning: Methods and Applications. Found. Trends® Signal Process. 7, 197–387 (2014).
[4]. Kamilaris, A. & Prenafeta-Boldú, F. X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90 (2018).
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[6]. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
[7]. Myers, L. & Sirois, M. J. Spearman Correlation Coefficients, Differences between. in Encyclopedia of Statistical Sciences (John Wiley & Sons, Ltd, 2006). doi:10.1002/0471667196.ess5050.pub2.
[8]. Benesty, J., Chen, J. & Huang, Y. On the Importance of the Pearson Correlation Coefficient in Noise Reduction. IEEE Trans. Audio Speech Lang. Process. 16, 757–765 (2008).
[9]. Sedgwick, P. Pearson’s correlation coefficient. BMJ 345, e4483 (2012).
[10]. Ahuja, R., Solanki, A. & Nayyar, A. Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor. in 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) 263–268 (2019). doi:10.1109/CONFLUENCE.2019.8776969.
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[12]. Guo, G., Wang, H., Bell, D., Bi, Y. & Greer, K. KNN Model-Based Approach in Classification. in On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE (eds. Meersman, R., Tari, Z. & Schmidt, D. C.) 986–996 (Springer, 2003). doi:10.1007/978-3-540-39964-3_62.
[13]. Jain, A. K., Mao, J. & Mohiuddin, K. M. Artificial neural networks: a tutorial. Computer 29, 31–44 (1996).
[14]. Krogh, A. What are artificial neural networks? Nat. Biotechnol. 26, 195–197 (2008).
[15]. Li, Z., Liu, F., Yang, W., Peng, S. & Zhou, J. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Trans. Neural Netw. Learn. Syst. 33, 6999–7019 (2022).
[16]. Gu, J. et al. Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018).
[17]. Staudemeyer, R. C. & Morris, E. R. Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks. Preprint at https://doi.org/10.48550/arXiv.1909.09586 (2019).
[18]. Smagulova, K. & James, A. P. A survey on LSTM memristive neural network architectures and applications. Eur. Phys. J. Spec. Top. 228, 2313–2324 (2019).
[19]. Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Preprint at https://doi.org/10.48550/arXiv.1412.3555 (2014).
[20]. Zhao, R. et al. Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks. IEEE Trans. Ind. Electron. 65, 1539–1548 (2018).
[21]. Vaswani, A. et al. Attention Is All You Need. (2017) doi:10.48550/ARXIV.1706.03762.
[22]. Chlap, P. et al. A review of medical image data augmentation techniques for deep learning applications. J. Med. Imaging Radiat. Oncol. 65, 545–563 (2021).
[23]. A survey of transfer learning | Journal of Big Data | Full Text. https://journalofbigdata.springeropen.com/articles/10.1186/s40537-016-0043-6.
[24]. Creswell, A. et al. Generative Adversarial Networks: An Overview. IEEE Signal Process. Mag. 35, 53–65 (2018).
Cite this article
Liu,L. (2023). Deep learning methods used in movie recommendation systems. Applied and Computational Engineering,15,149-154.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Marappan, R. & Bhaskaran, S. Movie Recommendation System Modeling Using Machine Learning. Int. J. Math. Eng. Biol. Appl. Comput. 12–16 (2022).
[2]. Goyani, M. & Chaurasiya, N. A Review of Movie Recommendation System: Limitations, Survey and Challenges. ELCVIA Electron. Lett. Comput. Vis. Image Anal. 19, 18–37 (2020).
[3]. Deng, L. & Yu, D. Deep Learning: Methods and Applications. Found. Trends® Signal Process. 7, 197–387 (2014).
[4]. Kamilaris, A. & Prenafeta-Boldú, F. X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90 (2018).
[5]. He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. (2015).
[6]. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
[7]. Myers, L. & Sirois, M. J. Spearman Correlation Coefficients, Differences between. in Encyclopedia of Statistical Sciences (John Wiley & Sons, Ltd, 2006). doi:10.1002/0471667196.ess5050.pub2.
[8]. Benesty, J., Chen, J. & Huang, Y. On the Importance of the Pearson Correlation Coefficient in Noise Reduction. IEEE Trans. Audio Speech Lang. Process. 16, 757–765 (2008).
[9]. Sedgwick, P. Pearson’s correlation coefficient. BMJ 345, e4483 (2012).
[10]. Ahuja, R., Solanki, A. & Nayyar, A. Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor. in 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) 263–268 (2019). doi:10.1109/CONFLUENCE.2019.8776969.
[11]. Ahmed, M., Seraj, R. & Islam, S. M. S. The k-means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics 9, 1295 (2020).
[12]. Guo, G., Wang, H., Bell, D., Bi, Y. & Greer, K. KNN Model-Based Approach in Classification. in On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE (eds. Meersman, R., Tari, Z. & Schmidt, D. C.) 986–996 (Springer, 2003). doi:10.1007/978-3-540-39964-3_62.
[13]. Jain, A. K., Mao, J. & Mohiuddin, K. M. Artificial neural networks: a tutorial. Computer 29, 31–44 (1996).
[14]. Krogh, A. What are artificial neural networks? Nat. Biotechnol. 26, 195–197 (2008).
[15]. Li, Z., Liu, F., Yang, W., Peng, S. & Zhou, J. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Trans. Neural Netw. Learn. Syst. 33, 6999–7019 (2022).
[16]. Gu, J. et al. Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018).
[17]. Staudemeyer, R. C. & Morris, E. R. Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks. Preprint at https://doi.org/10.48550/arXiv.1909.09586 (2019).
[18]. Smagulova, K. & James, A. P. A survey on LSTM memristive neural network architectures and applications. Eur. Phys. J. Spec. Top. 228, 2313–2324 (2019).
[19]. Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Preprint at https://doi.org/10.48550/arXiv.1412.3555 (2014).
[20]. Zhao, R. et al. Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks. IEEE Trans. Ind. Electron. 65, 1539–1548 (2018).
[21]. Vaswani, A. et al. Attention Is All You Need. (2017) doi:10.48550/ARXIV.1706.03762.
[22]. Chlap, P. et al. A review of medical image data augmentation techniques for deep learning applications. J. Med. Imaging Radiat. Oncol. 65, 545–563 (2021).
[23]. A survey of transfer learning | Journal of Big Data | Full Text. https://journalofbigdata.springeropen.com/articles/10.1186/s40537-016-0043-6.
[24]. Creswell, A. et al. Generative Adversarial Networks: An Overview. IEEE Signal Process. Mag. 35, 53–65 (2018).