Recommendation Systems and Filter Bubbles
Recommendation systems use data, such as viewing history, to suggest things a person might like. Suggestions can save search time or introduce something new. Narrow suggestions can contribute to a filter bubble that limits exposure to different topics or views.
What a learner can do afterwards
- Explain how viewing history or a click can provide clues for recommendations without proving a person's preferences
- Describe a filter bubble as recommendations narrowing exposure to different topics or views
- Give one benefit and one risk of recommendations, and describe a way to seek different material
1 · Read
Data is information computers use. Your spoken words for a voice assistant are data, and so is a record of videos you watched. A recommendation system uses data to suggest things you might like. Apps such as YouTube and Netflix can use watched videos as clues. Repeated topics can form a pattern. Suggestions are guesses. Systems can also use other clues and suggest unfamiliar topics.
Mia watches cooking shows on Netflix. Her viewing history gives a clue that she might enjoy another cooking show, even without searching for one. A shop can use a product click as a clue too. One click does not prove a lasting interest.
Recommendations can narrow what you see. If suggestions keep showing similar topics or views, you may miss different ones. This narrowing is called a filter bubble. Similar suggestions do not prove that you never see anything else.
Suggestions can save search time and help you find something enjoyable, including something new. A risk of narrow suggestions is missing other topics or views. With an adult, search for a different topic instead of picking only suggested items. This can help you look beyond the usual list.
Recommendations use data to make guesses. They can help you find things, but narrow suggestions may leave other topics or views out of sight.
Look beyond the suggestion list was written for this dataset.
More to read
Look beyond the suggestion list
Imagine a suggestion list filled with drawing videos. You enjoy drawing, so one of them might be useful. The list could save you time searching.
What is missing? Perhaps you also want to learn about birds. A list of drawing videos does not show every topic you could explore.
An app can use viewing history to choose suggestions. Repeated drawing videos give it a clue about your interests. Its guess may fit, but it does not know every reason you watched. One click does not prove what you will always like.
Recommendations can introduce unfamiliar things too. But if suggestions keep returning to similar topics or views, they can narrow what you see. This can contribute to a filter bubble.
With an adult, search for a different topic. Compare the results with the suggestions. You can use a suggestion when it helps and still look elsewhere.
2 · Watch
Take it off screen
Where it sits
This opens up
Where this leads
Jobs that lean on this skill. Follow one to see everything it is built on.
8 questions wait behind this lesson, each with its answer explained. Every answer feeds the sky: stars light as they are learned, and dim when it is time to come back.