Altering user Recommendations Using Generative AI
Year: 2025
Type: article
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.
Authors: M. Venkata Sai Pranav, K. Sai Rahul Reddy, K. Rakesh Kumar, A. Sai Vishal Krishna, Dr. Divya
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4409380725