Amazon's recommendation secret

In recent classes, we are discussing the recommender systems. In my mind, the most successful company using the recommender system is Amazon. All Amazon's users often receive emails, which recommend the items users are likely prefer and even buy.


At root, the retail giant’s recommendation system is based on a number of simple elements: what a user has bought in the past, which items they have in their virtual shopping cart, items they’ve rated and liked, and what other customers have viewed and purchased. Amazon calls this homegrown math “item-to-item collaborative filtering,” and it’s used this algorithm to heavily customize the browsing experience for returning customers. A gadget enthusiast may find Amazon web pages heavy on device suggestions, while a new mother could see those same pages offering up baby products.


Judging by Amazon’s success, the recommendation system works. The company reported a 29% sales increase to $12.83 billion during its second fiscal quarter, up from $9.9 billion during the same time last year. A lot of that growth arguably has to do with the way Amazon has integrated recommendations into nearly every part of the purchasing process from product discovery to checkout. Go to Amazon.com and you’ll find multiple panes of product suggestions; navigate to a particular product page and you’ll see areas plugging items “Frequently Bought Together” or other items customers also bought. The company remains tight-lipped about how effective recommendations are. (“Our mission is to delight our customers by allowing them to serendipitously discover great products,” an Amazon spokesperson told Fortune. “We believe this happens every single day and that’s our biggest metric of success.”)