Which machine learning algorithm, developed in 2013, revolutionized retail product recommendations by using neural networks to analyze customer browsing patterns rather than just purchase history?

Machine learning has revolutionized how retail companies analyze customer data, predict trends, and optimize operations. From recommendation algorithms to inventory management, the science behind these advancements represents some of the most sophisticated applications of AI in the commercial sector. Test your knowledge about a significant scientific breakthrough in retail machine learning that changed how retailers understand consumer behavior.

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Word2Vec by Google, which allowed retailers to map semantic relationships between products based on how customers viewed them
XGBoost by Tianqi Chen, which provided retailers with gradient-boosted decision trees for more accurate purchase predictions
DeepSense by Amazon, which used convolutional neural networks to analyze in-store customer movement patterns
RetailNet by Microsoft, which pioneered reinforcement learning for dynamic pricing based on real-time inventory levels