Which pioneering recommendation algorithm, introduced by Amazon in the late 1990s, revolutionized retail by analyzing item-to-item correlations rather than customer-to-customer similarities?

In today's data-driven retail landscape, recommendation systems have become crucial tools for increasing sales and enhancing customer experience. These sophisticated algorithms analyze past purchasing behavior, browsing history, demographic data, and even contextual information to suggest products that customers are likely to purchase. But how much do you know about the technological innovations behind these systems? Test your knowledge about the evolution and impact of retail recommendation algorithms!

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Collaborative Filtering Matrix (CFM)
Item-to-Item Collaborative Filtering
Neural Preference Analysis (NPA)
Basket Affinity Optimization (BAO)