Deep learning methods used in movie recommendation systems

Research Article
Open access

Deep learning methods used in movie recommendation systems

Lexi Liu 1*
  • 1 Chengdu Foreign Languages School    
  • *corresponding author 1502806106@qq.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/15/20230826
ACE Vol.15
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-021-9​
ISBN (Online): 978-1-83558-022-6

Abstract

As the amount of internet movie data grows rapidly, traditional movie recommendation systems face increasing challenges. They typically rely on statistical algorithms such as item-based or user-based collaborative filtering. However, these algorithms struggle to handle large-scale data and often fail to capture the complexity and contextual information of user behavior. Therefore, deep learning techniques have been widely applied to movie recommendation systems. This paper reviews movie recommendation algorithms based on traditional statistical models and introduces three main deep learning techniques: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). ANN can extract features at different levels of users and movies; CNN can capture features of movie posters and movie data to recommend similar movies; RNN can consider user historical behavior and contextual information to better understand user interests and demands. The application of these deep learning techniques can enhance the accuracy and user experience of movie recommendation systems. This paper also demonstrates the advantages and disadvantages of these models and their specific application methods in movie recommendation systems, and points out the direction for further development and improvement of deep learning models in this field.

Keywords:

recommendation system, deep learning, artificial neural network, CNN, RNN

Liu,L. (2023). Deep learning methods used in movie recommendation systems. Applied and Computational Engineering,15,149-154.
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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).


Cite this article

Liu,L. (2023). Deep learning methods used in movie recommendation systems. Applied and Computational Engineering,15,149-154.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN:978-1-83558-021-9​(Print) / 978-1-83558-022-6(Online)
Editor:Marwan Omar, Roman Bauer, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.15
ISSN:2755-2721(Print) / 2755-273X(Online)

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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).