Issue #42 Foundation Edwin Dong
Recommender Systems, Simply Explained
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In the last decade, much of the world has moved online, creating large-scale online services—changing the way we read news, buy products, and watch movies.
The abundance of items (and choices) online needed systems that could personalize our experience by discovering items tailored to our individual preferences. This led to the creation of recommender systems, machine learning models that use data about a user’s past behavior to predict what items (like products, movies, or music) they might be interested in.
They can then use this data to suggest and recommend relevant items to consumers, saving consumers time and reducing the problem of information overload—while also adding business value by optimizing sales and personalizing the customer experience.
Recommender systems are used by top companies such as Spotify, Apple, Amazon, Netflix, and YouTube, where they have enormous amounts of content (petabytes of data); these systems keep users interested.
For instance, take YouTube, where 500 hours of videos are uploaded every minute: it would take 82 years for a user to watch the videos uploaded in just the last hour.
On Spotify, users can listen to more than 80 million songs and podcasts. There are over 350 million different products listed on Amazon.
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Explicit Feedback and Implicit Feedback
In recommender systems, machine learning models are used to predict the rating (rᵤᵢ) of a user (u) and some item (i).
