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A recommender system can suggest items or actions of interest to a user, after having learned the user’s preferences over time. The technology, which is based on data and machine learning techniques (both supervised and unsupervised), has appeared on the Internet for about two decades.

Today you can find recommender systems almost everywhere, and they’re likely to play an even larger role in the future under the guise of personal assistants, such as Siri (developed by Apple), Amazon Alexa, Google Home, or some other artificial-intelligence–based digital assistant. The drivers for users and companies to adopt recommender systems are different but complementary:

  • Users: Have a strong motivation to reduce the complexity of the modern world (regardless of whether the issue is finding the right product or a place to eat) and avoid information overload.
  • Companies: Need recommender to systems provide a practical way to communicate in a personalized way with their customers and successfully push sales.

Recommender systems actually started as a means to handle information overload. The Xerox Palo Alto Research Center built the first recommender in 1992. Named Tapestry handled the increasing number of emails received by center researchers.

The idea of collaborative filtering was born by learning from users and leveraging similarities in preferences. The GroupLens project soon extended recommender systems to news selection and movie recommendations (the MovieLens project).

When giant players in the e-commerce sector, such as Amazon, started adopting recommender systems, the idea went mainstream and spread widely in e-commerce. Netflix did the rest by promoting recommenders as a business tool and sponsoring a competition to improve its recommender system that involved various teams for quite a long time. The result is an innovative recommender technology that uses SVD and Restricted Boltzmann Machines (a kind of unsupervised neural network).

However, recommender systems aren’t limited to promoting products. Since 2002, a new kind of Internet service has made its appearance: social networks such as Friendster, Myspace, Facebook, and LinkedIn. These services promote exchanges between users and share information such as posts, pictures, and videos.

In addition, these services help create links between people with similar interests. Search engines, such as Google, amassed user response information to offer more personalized services and understand how to match user’s desires when responding to users’ queries better.

Recommender systems have become so pervasive in guiding people’s daily life that experts now worry about the impact on our ability to make independent decisions and perceive the world in freedom.

A recommender system can blind people to other options — other opportunities — in a condition called filter bubble. By limiting choices, a recommender system can also have negative impacts, such as reducing innovation. You can read about this concern in the articles at dorukkilitcioglu.com and technologyreview.com.

One detailed study of the effect, entitled “Exploring the Filter Bubble: the Effect of Using Recommender Systems on Content Diversity,” appears on ACM. The history of recommender systems is one of machines striving to learn about our minds and hearts, to make our lives easier, and to promote the business of their creators.

About This Article

This article is from the book:

About the book authors:

John Mueller has published more than 100 books on technology, data, and programming. John has a website and blog where he writes articles on technology and offers assistance alongside his published books.

Luca Massaron is a data scientist specializing in insurance and finance. A Google Developer Expert in machine learning, he has been involved in quantitative analysis and algorithms since 2000.

John Mueller has published more than 100 books on technology, data, and programming. John has a website and blog where he writes articles on technology and offers assistance alongside his published books.

Luca Massaron is a data scientist specializing in insurance and finance. A Google Developer Expert in machine learning, he has been involved in quantitative analysis and algorithms since 2000.

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