Deep Learning-Based Web Crawler Page Rank Algorithm for Enhanced Search Relevance

Praveenkumar G D; Sadhanayaki S

doi:10.32628/IJSRST52411240

Authors

Praveenkumar G D Assistant Professor, Department of CT – UG, Kongu Engineering College –Erode, Tamilnadu, India Author
Sadhanayaki S Assistant Professor, Department of Computer Technology and Information Technology, Kongu Arts and Science College (Autonomous), Erode, Tamilnadu, India Author

DOI:

https://doi.org/10.32628/IJSRST52411240

Keywords:

Web Page, Web Crawler, Feature Extraction, RNN, Page Rank Algorithm

Abstract

A web crawler page rank algorithm employing deep learning techniques aims to revolutionize the process of indexing and ranking web pages by leveraging neural networks. By analyzing content, context, and user behavior patterns, the algorithm improves relevance, adapts dynamically to evolving web content, enhances user experience, scales efficiently, and remains robust against manipulation. This approach promises to deliver more enhance the accuracy and efficiency of web crawlers in ranking web pages.

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