• K. Rohit Sai
• Konda Sandeep
• Harsh Amilpur
• Byagari Gowthami
• Anil Kumar

The primary goal of this study is to reduce noise in user search results using feedback-based adaptive algorithms and preference-based filtering techniques. The project aims to improve user searching by developing a system that allows users to participate in the noise reduction process. It does this by utilizing deep learning algorithms, natural language processing, and the Google Search API. The iterative feedback loop module applies feedback-driven learning by continuously optimizing the system through user interactions. The user interface module offers an easy-to-use interface for feedback and data presentation. To cut through noise in the data and yield more targeted results, it makes use of site-specific search features, user profiling, keyword specific, period specific, and file formats. Over time, the model performs better thanks to all of these features. and offers an improved user experience.

Noise reduction, User search results, deep learning, natural language processing, Google Search API, iterative feedback loop, user interactions, site-specific search, user profiling, keyword specific, file formats.

"User Specific Web Data Refinement : An Adaptive Learning System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.d21-d27, April-2024, Available :http://www.jetir.org/papers/JETIR2404305.pdf

"User Specific Web Data Refinement : An Adaptive Learning System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppd21-d27, April-2024, Available at : http://www.jetir.org/papers/JETIR2404305.pdf