Which scientific principle is NOT commonly used in modern retail personalization algorithms that target customers of scientific equipment and laboratory supplies?

In today's data-driven retail landscape, personalization algorithms are becoming increasingly sophisticated. These algorithms analyze customer behavior patterns and preferences to create tailored shopping experiences that feel almost intuitive. Behind these seemingly magical recommendations lies complex science - from machine learning to behavioral psychology. Test your knowledge about the cutting-edge technology that powers modern retail personalization systems used by scientific equipment suppliers, pharmaceutical retailers, and tech companies.

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Bayesian probability models to predict future purchases based on past behavior
Quantum entanglement theory to create synchronized shopping recommendations across different devices
Neural networks that identify patterns in browsing and purchasing history
Collaborative filtering systems based on similarity coefficients between customer profiles