Adaptive Web Recommendations For Personalized Browsing

Abstract

The Approach Extracts Key Features From Web Documents To Form Concepts, Which Are Used To Build An Ontology Capturing Semantic Relationships Between Learning Materials. Web Usage Mining Analyzes Learner Interactions And Navigation Patterns, While Semantic Clustering Groups Content Based On Similarity To Identify Learner Preferences And Difficulties. By Combining Semantic Knowledge With Usage Data, The System Provides Personalized And Context-aware Recommendations Tailored To Each Learner’s Understanding. Experimental Results Show That The System Effectively Supports Learner Review By Recommending Relevant Content Aligned With Individual Needs. It Helps Learners Focus On Weak Areas, Improves Engagement, Reduces Cognitive Overload, And Enhances Overall Comprehension. This Framework Demonstrates The Effectiveness Of Integrating Ontology With Web Usage Mining For Adaptive Learning Systems.

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