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References

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep

bidirectional transformers for language understanding. Proceedings of the 2019

Conference of the North American Chapter of the Association for Computational

Linguistics: Human Language Technologies , 1, 4171 – 4186.

https://doi.org/10.18653/v1/N19-1423

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9 (1),

1 – 42. https://doi.org/10.1162/neco.1997.9.1.1

Hollywood movies dataset. (n.d.). Kaggle. Retrieved July 14, 2025, from

https://www.kaggle.com/datasets/mohdmuttalib/hollywood-movies-dataset

Introduction to recommender systems, content-based, collaborative filtering and hybrid

recommendation engines – Data science, machine learning, deep learning. (n.d.). Alpha

Quantum. https://www.alpha-quantum.com/blog/recommender-systems/introduction

to-recommender-systems-content-based-collaborative-filtering-and-hybrid

recommendation-engines

Lee, S., Kc, B., & Choeh, J. Y. (2020). Comparing performance of ensemble methods in predicting

movie box office revenue. Heliyon, 6 (6), e04260.

https://doi.org/10.1016/j.heliyon.2020.e04260

Liu, Q., Hu, J., Xiao, Y., Zhao, X., Gao, J., Wang, W., Li, Q., & Tang, J. (2024). Multimodal

recommender systems: A survey. ACM Computing Surveys .

https://doi.org/10.1145/3695461

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