Recommendation Systems
MK and
Recommendation Systems
In the rush to copy TikTok, many Western social networks have misread how easy it is to apply lessons of a very particular short video experience to social feeds built around other formats. If you’re Instagram Reels and your format and interface are a near carbon copy, then sure, applying the lessons of my three TikTok essays is straightforward.... See more
Eugene Wei • How to Blow Up a Timeline
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
Constructor: Holistic Product Discovery for Ecommerce Results
constructor.io
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
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