Which mathematical technique is MOST commonly used as the foundation for retail recommendation engines that suggest products based on purchase history?

In today's data-driven retail environment, sophisticated mathematical algorithms power personalized shopping experiences. These algorithms analyze customer behavior patterns, purchase history, and demographic information to create tailored recommendations and targeted marketing. How well do you understand the mathematical foundations that drive modern retail personalization systems? Test your knowledge about the intersection of mathematics and retail personalization technology!

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Collaborative filtering using matrix factorization and cosine similarity metrics
Bayesian network analysis with conditional probability tables
Euclidean distance calculations in high-dimensional vector spaces
Markov decision processes with reinforcement learning optimization