Abstract

This study presents the Contextually Aware Personalized Information Retrieval (CAPIR) system. It is designed to address the limitations of traditional information retrieval (IR) models, which often rely on static keyword-based methods that neglect user context. CAPIR combines contextual awareness with evolutionary machine learning (EML), dynamically adapting to factors such as user behavior, location, and time. This approach improves the precision and relevance of search results by continuously refining retrieval strategies based on user interactions and feedback. Quantitative evaluation using precision, recall, and Mean Average Precision (MAP) showed significant improvements over traditional IR models, while qualitative feedback highlighted CAPIR’s adaptability to evolving user needs. CAPIR’s framework and experimental validation demonstrate its potential as a robust solution for environments requiring flexible and adaptive IR capabilities.

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