Which scientific algorithm was NOT traditionally used in early e-commerce recommendation engines?

Recommendation engines have revolutionized the online shopping experience by analyzing customer data and suggesting relevant products. These sophisticated algorithms leverage various scientific approaches to predict consumer preferences. But how well do you understand the technology powering the recommendations you see every day? Test your knowledge about the science behind retail recommendation systems that influence billions of dollars in purchasing decisions.

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Collaborative filtering based on user similarity metrics
Content-based filtering using product feature analysis
Quantum computing optimization techniques
Association rule learning (market basket analysis)