Multi-Phase Recommender Framework for Pattern Warehousing Using Evolutionary Optimization

With the continuous surge in organizational data, the need for efficient storage and extraction of valuable insights has become increasingly significant. Pattern warehousing serves as a critical component in this context. However, identifying and recommending the most relevant patterns from vast and intricate datasets remains a core challenge. This study introduces a multi-phase recommender framework that leverages evolutionary optimization to enhance the accuracy and relevance of pattern recommendations. By integrating Genetic Algorithm (GA) into the recommendation pipeline, the system facilitates intelligent pattern selection and minimizes unnecessary computations through automated optimization. The use of GA enables the system to evolve over iterations, dynamically refining its pattern selection process based on fitness evaluation, which helps in consistently identifying high-utility patterns. The proposed solution undergoes a comprehensive evaluation using real-world datasets associated with pattern warehousing. Diverse datasets from different domains were utilized to access the system’s flexibility, including transactional and user-rating based environments. Experimental findings confirm that the evolutionary approach outperforms traditional methods by delivering high-quality recommendations. The framework demonstrates strong adaptability to evolving landscapes, supporting better decision-making and reinforcing the strategic value of the pattern warehouse.