Authors : M. Venkata Sai Pranav; K. Sai Rahul Reddy; K. Rakesh Kumar; A. Sai Vishal Krishna; Dr. Divya

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/3nesk7at

Scribd : https://tinyurl.com/2pudcb77

DOI : https://doi.org/10.38124/ijisrt/25mar1778

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Abstract : Enhancing user experiences on video platforms such as YouTube requires personalised content discovery. Repetitive or inappropriate suggestions are produced by current recom- mendation systems, which frequently rely on engagement-based data. In order to provide a more individualised and distraction- free experience, this study presents an innovative method that uses generative AI to optimise search queries and automate video navigation. The solution improves content relevance, fil- ters distractions like Shorts, and expedites video selection by combining Google Cloud deployment, Chrome Extension-based automation, and AI-driven query generation. The findings of the experiment show that the suggested framework is effective in improving content recommendations, with 92% accuracy in query personalisation, 95% success in automated navigation, and 98% accuracy in distraction filtering.

Keywords : Generative AI, Personalized Recommendations, Automation, Content Filtering, YouTube API, Chrome Exten- sions, Machine Learning, Cloud Computing.

References :

1. J. Smith, R. Doe, and A. Brown, “Personalized video recommendation using deep learning,” IEEE Transactions on Multimedia, vol. 60, no. 4, pp. 345–356, 2024.
2. P. Kumar, L. Sharma, and M. Patel, “Generative ai for search query refinement,” Springer AI Journal, vol. 12, no. 2, pp. 122–134, 2024.
3. K. Anderson, S. Lee, and H. Taylor, “Automating youtube interactions using ai,” IEEE Transactions on Artificial Intelligence, vol. 10, no. 3, pp. 234–245, 2024.
4. B. Miller, T. Chan, and V. Gupta, “Ai-based recommendation models for content discovery,” Neural Processing Letters, vol. 24, no. 5, pp. 1104–1120, 2024.
5. R. Wilson, A. Moore, and K. Tran, “Enhancing video recommendations with cloud-based ai,” Nature Communications, vol. 13, no. 2, pp. 556– 568, 2024.
6. L. Jones, T. Kim, and S. Park, “Benchmarking ai-based recommender systems for video platforms,” arXiv preprint, 2024.
7. A. Roberts, J. Wang, and P. Singh, “Ai in video analysis, production, and streaming delivery,” ResearchGate, 2024.
8. K. Patel, D. Rao, and S. Gupta, “A review on the role of generative ai in personalization and recommendation systems,” Springer Journal of AI Applications, vol. 12, no. 4, pp. 345–358, 2024.
9. H. Brown, M. Green, and E. Davis, “An intelligent video editing framework using ai and computer vision,” SSRN Preprint, 2024.
10. T. White, R. Black, and L. Perez, “Generative ai for content personal- ization,” 2024.
11. Y. Zhang, C. Ning, and W. Yang, “Deep learning in recommendation systems: A comparative study,” Scientific Reports, vol. 15, no. 1, p. 3240, 2024.
12. M. Talo, “Automated search optimization using generative ai,” Journal of Applied AI Research, vol. 18, no. 3, pp. 77–91, 2024.
13. W. Thompson, J. Parker, and L. Nguyen, “Distraction-free video recom- mendation: Ai-based filtering techniques,” Journal of AI and Multimedia Applications, vol. 14, no. 2, pp. 199–213, 2024.
14. N. Fischer, B. Carter, and Z. Lin, “A comparative analysis of transformer-based recommender systems,” Elsevier AI Review, vol. 22, no. 1, pp. 56–73, 2024.