Various desktop/ mobile applications (say Instagram, Netflix, Youtube, etc) give recommended content to a user.
How are they able to do that? I wanted to learn what’s the technical aspect that goes behind it.
In: 4
in principal it is really quite simple. When someone clicks on a video, watch what video they go to next, and which video they came from. keep a count of this for everyone
when someone else clicks on the same first video, if they haven’t seen the video most people go to/came from, recommend that to them.
In practice, its quite complicated since you want to filter out noise from the system, and not everyone is the same. So you try to use an “algorithm” to build up a profile of each individual, and what they like/dont like, and what people like them like/dont like. Then, recommend them things people like them have liked (feel free to pick whatever metric you want for what “liked” means, just know that whatever you pick, that content will do better on your platform) (algorithm too, and when in doubt throw marketing keywords at it like “big data”, “machine learning” or “AI Driven”).
From a technical “how do you get the data”, you are logged into these sites, they have your entire watch history and how far you got in each one (so you can continue watching it) all the data is there for the taking. a lot of people here talk about the tracking cookies, and they do uses those for people without an account, but once you are logged in, the tracking cookie is basically useless (unless you go off site).
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