Which mathematical approach forms the foundation of most modern retail personalization algorithms that recommend products based on previous purchases?

In today's data-driven retail environment, sophisticated mathematical algorithms power personalization systems that analyze customer behavior and preferences. These systems use various statistical and machine learning techniques to provide tailored shopping experiences. How well do you understand the mathematical foundations behind these powerful retail personalization tools?

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Bayesian probability networks that calculate conditional likelihood of purchases
Collaborative filtering using matrix factorization and cosine similarity metrics
Euclidean distance calculations in multi-dimensional product attribute space
Markov chain models that predict sequential purchasing patterns