Applications of Quantum Machine Learning in Large-Scale E-commerce Recommendation Systems: Enhancing Efficiency and Accuracy
DOI:
https://doi.org/10.5281/zenodo.13117899ARK:
https://n2t.net/ark:/40704/JIEAS.v2n4a14Keywords:
Quantum Machine Learning, E-commerce Recommendation Systems, Quantum Principal Component Analysis, Scalable Quantum AlgorithmsAbstract
This paper presents a novel quantum-enhanced recommendation system for large-scale e-commerce platforms, addressing the challenges of computational complexity and scalability in traditional approaches. We introduce a hybrid quantum-classical architecture that leverages quantum principal component analysis (qPCR) for efficient feature extraction and quantum similarity computation for improved recommendation accuracy. Our system demonstrates significant performance improvements over classical methods, achieving an 87.3% reduction in execution time and a 15.8% increase in precision@10 across diverse e-commerce datasets. We implement our approach on simulated quantum devices, evaluating their performance on the Amazon Product Reviews, MovieLens 20M, and Yelp Dataset Challenge datasets. The quantum-enhanced system exhibits logarithmic growth in execution time with increasing dataset size compared to the near-linear growth of classical systems. We comprehensively analyze the system's computational complexity, scalability, and accuracy metrics, including MAP and NDCG. Additionally, we discuss the current limitations of quantum hardware and propose strategies for integrating quantum-enhanced recommendations into existing e-commerce infrastructures. Our findings highlight the potential of quantum computing to revolutionize personalized recommendations in e-commerce, paving the way for future research in quantum-enhanced machine learning for large-scale data processing and decision-making systems.
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