• Corpus ID: 7723585

Two Decades of Recommender Systems at Amazon

@inproceedings{Blake2017TwoDO,
  title={Two Decades of Recommender Systems at Amazon},
  author={M. Brian Blake},
  year={2017}
}

• M. B. Blake
• Published 2017
• Computer Science

F or two decades now, 1 Amazon. com has been building a store for every customer. Each person who comes to Amazon.com sees it differently, because it’s individually personalized based on their interests. It’s as if you walked into a store and the shelves started rearranging themselves, with what you might want moving to the front, and what you’re unlikely to be interested in shuffling further away. From a catalog of hundreds of millions of items, Amazon.com’s recommendations pick a small number of items you might enjoy based on your current context and your past behavior. The algorithms aren’t magic; they simply share with you what other people have already discovered. The algorithm does all the work. It’s computers helping people help other people, implicitly and anonymously. Amazon.com launched item-based collaborative filtering in 1998, enabling recommendations at a previously unseen scale for millions of customers and a catalog of millions of items. Since we wrote about the algorithm in IEEE Internet Computing in 2003,2 it has seen widespread use across the Web, including YouTube, Netflix, and many others. The algorithm’s success has been from its simplicity, scalability, and often surprising and useful recommendations, as well as desirable properties such as updating immediately based on new information about a customer and being able to explain why it recommended something in a way that’s easily understandable. What was described in our 2003 IEEE Internet Computing article has faced many challenges and seen much development over the years. Here, we describe some of the updates, improvements, and adaptations for item-based collaborative filtering, and offer our view on what the future holds for collaborative filtering, recommender systems, and personalization.