Which mathematical concept is NOT typically used in retail recommendation systems that personalize product suggestions for customers?

In today's data-driven retail environment, mathematical models power personalized recommendation systems. These algorithms analyze purchase patterns, browsing behavior, and demographic data to suggest products customers are likely to buy. Test your knowledge about the mathematical foundations behind these powerful retail recommendation engines that have transformed how customers discover products and how retailers optimize their offerings.

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Markov Decision Processes for sequential purchase predictions
Euclidean Distance calculations for similarity metrics
Non-negative Matrix Factorization for collaborative filtering
Gödel's Incompleteness Theorems for preference consistency