Which algorithmic approach revolutionized retail recommendation systems around 2013, enabling significantly more accurate product suggestions?

Recommendation systems are crucial tools in modern retail, helping connect consumers with products they're likely to enjoy. These sophisticated algorithms analyze past purchases, browsing behavior, and demographic data to suggest items customers might want to buy next. But do you know which technological breakthrough dramatically improved these systems in the early 2010s? Test your knowledge of retail technology innovation and see if you can identify the game-changing approach that powers many of today's most effective recommendation engines.

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Deep neural networks using collaborative filtering
Bayesian probability models
Random forest decision trees
K-means clustering algorithms