Recommendation Systems
MK and
Recommendation Systems
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
... See moreNathan Baschez • Noise-Canceling Filters for the Internet are Coming
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as a network scales, it is inevitable that you need a way to sort & rank content.
web 2.0 solved it by ranking for popularity but a better approach could be to have a minimal quality threshold but not to try to rank things above that - so the search is not maximization-driven and leaves more room for serendipity.
h/t ken stanley
I’ve never cared much about the presence or not of a blue check by a user’s name, but I do notice when tweets from people I follow make up a smaller and smaller percentage of my feed. It’s as if neighbors of years moved out from my block overnight, replaced by strangers who all came knocking on my front door carrying not a casserole but a... See more
Eugene Wei • How to Blow Up a Timeline
I wonder whether/why a For You feed is harder to tune in text-based mediums than video ones. Regardless, agree that the Twitter For You feed doesn't deliver.
