AI-Driven Personalized Movie Recommendations: A Content and Sentiment-Aware Model for Streaming and Digital Entrepreneurship

Vijay Shelake; Scott Fernandes; Sarthak Shrungare

doi:10.34306/att.v7i2.550

Authors

Vijay Shelake University of Mumbai https://orcid.org/0000-0003-2739-4110
Scott Fernandes University of Mumbai
Sarthak Shrungare University of Mumbai

DOI:

https://doi.org/10.34306/att.v7i2.550

Keywords:

AI, Sentiment Analysis, Content, Movie, Streaming

Abstract

In an era marked by the digital consumption of media, the landscape of movie recommendation is undergoing a profound transformation. Traditional recommendation methods, which rely on collaborative filtering and user reviews, are being supplanted by more sophisticated content-based approaches. The evolution of Artificial Intelligence (AI) has given rise to a new generation of recommendation systems, characterized by their ability to process and analyze vast amounts of content metadata to provide tailored suggestions. This research presents an AI-driven personalized movie recommendation model for streaming and digital entrepreneurship, leveraging data analytics and Natural Language Processing (NLP) techniques to enhance user experience. The model integrates sentiment analysis and cosine similarity to recommend similar movies, offering personalized recommendations across multiple streaming platforms, thus improving user satisfaction, engagement, and content discovery. By utilizing AI-driven algorithms, this model contributes to digital entrepreneurship by enhancing content personalization and improving user retention in the competitive streaming industry.

References

Y. Mu and Y. Wu, “Multimodal movie recommendation system using deep learning,” Mathematics, vol. 11, no. 4, p. 895, 2023.

R. Sharma and A. Gupta, “Hybrid approach for movie recommendation using collaborative filtering and content-based filtering,” in Proceedings of the International Conference on Artificial Intelligence and Data Science (ICAIDS), 2023.

E. Johnson and W. Zhang, “Deep learning approaches for movie recommendation systems,” IEEE Transactions on Knowledge and Data Engineering, 2023.

S. S. et al., “Advancing movie recommendations through graph neural networks,” IEEE Access, vol. 11, pp. 12 345–12 358, 2023.

R. Chen and T. Huang, “Explainable hybrid recommendation for movies using visual and textual features,” Journal of Artificial Intelligence Research, vol. 75, pp. 567–589, 2023.

Y. Patel and M. Kumar, “Cold start solutions for movie recommenders using deep learning and metadata,” in Proceedings of the 2023 International Conference on Advances in Computing and Artificial Intelligence (ACAI), 2023, pp. 203–212.

A. Gupta and J. Verma, “Nlp-powered movie recommendation system based on user reviews,” International Journal of Machine Learning and Computing, vol. 13, no. 7, pp. 890–899, 2023.

T. Yang et al., “Reinforcement learning for real-time movie recommendations,” Neural Computing and Applications, vol. 35, pp. 8901–8914, 2023.

A. Sharma, “Personalized recommendations with attention mechanisms in deep learning,” Elsevier Information Systems Journal, vol. 110, pp. 567–580, 2023.

K. Li and X. Zhang, “Efficient scalability in deep learning-based movie recommendation systems,” ACM Transactions on Knowledge Discovery from Data, vol. 17, no. 4, 2023.

M. Das and R. Bhattacharya, “Fusion of collaborative and content-based filtering using latent semantic analysis,” in Springer Lecture Notes on Artificial Intelligence, 2023.

M. Kim et al., “Real-time movie recommendation systems using federated learning,” IEEE Transactions on Big Data, 2023.

P. Mehta et al., “Sentiment-augmented recommendations in movie platforms,” IEEE Transactions on Computational Social Systems, 2023.

S. Chawla, S. Gupta, and R. Majumdar, “Movie recommendation models using machine learning,” in Proceedings of the 2021 5th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2021, pp. 1–6.

S. Siet, S. Peng, S. Ilkhomjon, M. Kang, and D.-S. Park, “Enhancing sequence movie recommendation system using deep learning and kmeans,” Applied sciences, vol. 14, no. 6, p. 2505, 2024.

H. Wang, N. Lou, and Z. Chao, “A personalized movie recommendation system based on lstm-cnn,” in Proceedings of the 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 2020, pp. 485–490.

R. Lavanya and B. Bharathi, “Systematic analysis of movie recommendation system through sentiment analysis,” in Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, 2021, pp. 614–620.

Y. Zhao et al., “Transformer-based models for personalized movie recommendations,” Journal of Big Data, vol. 10, no. 1, 2023.

J. Park, H. Lee, and M. Kim, “Contrastive learning in movie recommendation systems,” Expert Systems with Applications, vol. 211, 2023.

H. Liu and X. Zhou, “Meta-learning for cross-domain movie recommendations,” IEEE Transactions on Artificial Intelligence, vol. 4, no. 3, 2023.

A. Jones and R. Miller, “Self-supervised learning for cold start problem in movie recommendations,” ACM Transactions on Information Systems, vol. 41, no. 2, 2023.

L. Wang et al., “Bert4rec: A deep bidirectional model for movie recommendations,” in Proceedings of the ACM SIGIR Conference, 2023, pp. 450–458.

R. Singh, P. Sharma, and N. Gupta, “Ensemble learning techniques in movie recommendation systems,” Knowledge-Based Systems, vol. 257, 2023.

C. Brown, J. Lin, and W. Lee, “Adaptive movie recommendations using dynamic user modeling,” Elsevier Future Generation Computer Systems, vol. 153, pp. 734–747, 2023.

Y. Gao and H. Zhang, “Attention mechanisms for enhanced recommendation accuracy in movie systems,” IEEE Access, vol. 11, pp. 21 034–21 045, 2023.

S. Kim, M. Park, and T. Yoon, “Deep content embedding for movie recommendations using neural networks,” Springer Machine Learning and Applications, 2023.

D. Xu and X. Li, “Context-aware movie recommendation systems with knowledge graphs,” Information Processing and Management, vol. 60, no. 3, 2023.

S. Abirami, P. Muthuraman, C. Sathish, K. Sundharabalaji et al., “Personalized movie recommendations: A comprehensive review and analysis,” in 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1. IEEE, 2024, pp. 2042–2045.

S. Patil et al., “Enhancing movie recommendation systems with bert: A deep learning approach,” in 2024 5th International Conference for Emerging Technology (INCET). IEEE, 2024.

S. Watini, L. Magdalena, T. W. Wirjawan, A. Gunawan, D. Julianingsih, and N. Ivanov, “Social media as a tool for transforming childhood learning mechanisms in edupreneurship,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 1, pp. 109–119, 2025.

M. Khan et al., “Movie recommendation system using euclidean distance,” in 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024.

D. Cahyono, A. Susbiyani, E. Lestari, F. Fauziyah, N. Qomariah, and Y. S. Guntur, “Role of personal savings in financial tech impact on family planning in indonesia,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 1, pp. 120–131, 2025.

J. Kumar et al., “Movie recommendation system based on emotions,” in 2024 10th International Conference on Communication and Signal Processing (ICCSP). IEEE, 2024.