A HYBRID INTELLIGENT SEARCH ENGINE RANKING MODEL INTEGRATING WEB CONTENT, STRUCTURE, AND USAGE MINING FOR PERSONALIZED INFORMATION RETRIEVAL

Abstract

The rapid growth of the World Wide Web has resulted in an enormous volume of heterogeneous and dynamic information, making efficient information retrieval a significant challenge. Search engines serve as the primary mechanism for accessing web information; however, traditional ranking algorithms primarily rely on keyword matching and hyperlink structures, often failing to capture user preferences and contextual relevance. Web mining techniques offer an effective solution by extracting valuable knowledge from web content, web structures, and user interaction patterns. This paper proposes a Hybrid Intelligent Search Engine Ranking Framework (HISERM) that integrates Web Content Mining, Web Structure Mining, and Web Usage Mining with machine learning techniques to improve search result relevance and ranking quality. The proposed framework combines content-based features, link-based features, and user behavioral characteristics through a feature fusion mechanism and applies a Random Forest learning model to generate adaptive ranking scores. The methodology utilizes publicly available web datasets and evaluates ranking effectiveness using standard information retrieval metrics. The proposed approach aims to enhance personalization, reduce irrelevant search results, and improve user satisfaction. The framework provides a scalable and intelligent solution for next generation search engines capable of handling dynamic web environments and large-scale information repositories.