Which mathematical technique forms the foundation of most retail personalization algorithms that recommend products based on past customer behavior?

Personalized shopping experiences are now driven by sophisticated algorithms that analyze customer behavior patterns. Behind these seamless recommendations lies complex mathematics. This poll tests your knowledge about the algorithms that power retail personalization systems used by major e-commerce platforms. Understanding these mathematical foundations gives insight into how retailers create tailored experiences that drive customer loyalty and increase sales conversion rates.

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Collaborative filtering using matrix factorization
Bayesian probability with Monte Carlo simulations
Convolutional neural networks with image recognition
Euclidean distance calculations in k-means clustering