Optimizing E-Commerce Product Recommendations with Hybrid Clustering and Evolutionary Algorithms

Abstract

Implementing online recommendation systems is very important for both customer satisfaction improvement and companies' profit increase. Traditional methods, which include CBF and CF, have scalability issues, data sparsity, and cold-start problems. The work proposes an optimized hybrid clustering-based recommendation system with the use of evolutionary approaches. Particle Swarm Optimization will be used by the model in dynamic cluster optimization and combination of K-Means and Hierarchical Clustering for segmenting customers. Feature augmentation methods and graph-based embedding improve cold-start management and personalization. Besides outstanding improvements over normal DL-based models in recommendation accuracy, it also ensures outstanding performances on precision, recall, and F1-score, each over 99% improvement. Real-time latency and resource consumption will be guaranteed to be low thanks to computational efficiency and scalability. This work has demonstrated how the hybrid of clustering and evolutionary algorithms effectively overcomes the shortcomings of current recommendation systems and provides a reliable and scalable solution for modern e-commerce websites.