Which mathematical technique is NOT commonly used in retail personalization algorithms for product recommendations?

Retail personalization algorithms have transformed how mathematics is applied in consumer markets. These sophisticated systems analyze customer behavior patterns to deliver targeted product recommendations and personalized shopping experiences. Behind these systems are complex mathematical models including collaborative filtering, matrix factorization, and neural networks. This poll tests your knowledge of how mathematical principles drive modern retail personalization technology.

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• Quantum Polynomial Expansion
• Collaborative Filtering
• Markov Decision Processes
• Matrix Factorization

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