Business · NVIDIA Blog
Collaborative filtering helps you catch what you like by looking for users who are similar to you
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So while the recommender system may not know anything about your taste in music, if it knows you and another user share similar taste in books, it might recommend a song to you that it knows this other user already likes.
Key facts
- Jussi Karlgren formulated the idea of a recommender system, or a “digital bookshelf,” in 1990
- A business may not know what any one individual will do, but thanks to the law of large numbers, they know that, say, if an offer is presented to 1 million people, 1 percent will take it
- So if a recommender sees you liked the movies “You’ve Got Mail” and “Sleepless in Seattle,” it might recommend another movie to you starring Tom Hanks and Meg Ryan, such as “Joe Versus the Volcano
- The NVIDIA Deep Learning Institute offers instructor-led, hands-on training on the fundamental tools and techniques for building highly effective recommender systems
Summary
Spend enough time online, however, and what you want will start finding you when you need it. They’re called recommender systems, and they’re among the most important applications today. That’s because there is an explosion of choice and it’s impossible to explore the large number of available options. If a shopper were to spend one second each swiping on their mobile app through the two billion products available on one prominent ecommerce site, it would take 65 years — almost an entire lifetime — to go through their entire catalog. This is one of the major reasons why the Internet is now so personalized, otherwise it’s simply impossible for the billions of Internet users in the world to connect with the products, services, even expertise — among hundreds of billions of things — that matter to them.