The Engineering behind Instagram's Recommendation Algorithm
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The Engineering behind Instagram's Recommendation Algorithm
Social media algorithms are usually “adaptive,” which means they constantly make small changes to themselves in order to try to get better results; “better” in this case meaning more engaging and therefore more profitable. A little randomness is always present in this type of algorithm.
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Today most algorithms that recommend or suppress content act purely on the basis of inferred popularity. They look at how much time people spend engaging with a piece of content, and boost it to more people if the numbers look good. The content itself is almost purely a black box. Some algorithms try to classify content with tags like “food” or “fu
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in recommendation media, the algorithms that power distribution reign supreme. These algorithms, which are powered by machine learning, are unique, valuable, and grow in power and accuracy as a platform scales. Therefore, only the biggest and most powerful platforms can afford investments in the best machine learning algorithms because they are suc... See more
Michael Mignano • The End of Social Media
This helped scale the density of the LinkedIn network so that even after you added hundreds of connections, the site could still help recommend relevant people to you. This is a direct example of alleviating the overcrowding dynamics of a social network, which is exactly why people recommendations, relevance-driven feeds, trending topics, and a sle
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