This is just one of many challenges Zhang's team is trying to solve.ĭual embeddings of each node, as both source and target, and a novel loss function enable 30% to 160% improvements over predecessors. Some items are more likely to be selected by customers simply because they are placed at more visible locations. For example, if a product's placement influences a decision, that's a case of bias caused by position. ![]() ![]() Understanding customers’ preferences can be challenging in a noisy environment where customers may have a number of potential factors motivating a decision. Did you buy dog treats because you set out to do that or because seeing them reminded you to? ![]() "The complexity we're solving, and the impact we have, make our team a haven for scientists." Anticipating the customer's needsĪs every shopper knows, what you intend to do when you get to a store and what you actually do are often two different things. "Our goal is to understand the customer's intention for that moment and provide relevant experiences and products," she says. As director of research science for personalization strategic initiatives including Buy Again, Amazon Fresh, Amazon Business, Pets, and Beauty, Zhang applies deep learning for tailored, real-time recommendations to customers. Ren Zhang's team uses science to present customers with the most relevant offerings possible. The story of a decade-plus long journey toward a unified forecasting model.
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