Related Items Recommendations

Amazon Personalize Recipe: SIMS

Similar item recommendations help users discover new products or compare existing items in your catalog. Amazon Personalize recommends similar items in real-time, based on user behavior to create unique, relevant experiences for your customers.

Pretty simple idea, implemented via item-item collaborative filtering but basically look at how people are interacting with particular things and then determine how similar things are at a global level based on that data. Not user specific at all.

The similar item recommendations use case is implemented in all the product detail pages under “Compare similar items” carousel UI widget.

Customer use case : StockX

“StockX is a Detroit startup company revolutionizing ecommerce with a unique Bid/Ask marketplace—our platform models the New York Stock Exchange and treats goods like sneakers and streetwear as high-value, tradable commodities. With a transparent market experience, StockX provides access to authentic, highly sought-after products at true market price.”

“Recommended for You was a massive win for both our team and StockX as a whole. We’re quickly learning the potency of integrating ML into all facets of the company. Our success led to key decision-makers requesting we integrate Amazon Personalize into more of the StockX experience and expand our ML endeavors. It’s safe to say that personalization is now a first-class citizen here.”

Sam Bean and Nic Roberts II at StockX.

Read full AWS Machine Learning Blog: Pioneering personalized user experiences at StockX with Amazon Personalize