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
Cookies is one part. Sneaking/watching your pc is another. They use all this data and funnel it into an algorithm called “Recommender systems” (or recommendation systems).
There are three general approaches to it:
1. Collaborative filtering
2. Content-based filtering
3. Hybrid
For t#1, the general idea is that people who liked the same things in the past, would also like the same thing in the future. Those “people who liked XYZ also liked ABC” types of recommendations are usually collaborative filtering. For example, yt may realize that people who liked content creator XYZ in the past, tend to also like channel ABC. So if you watched XYZ, yt may suggest to you ABC.
For #2, the focus is on key features of the item you’re recommending. For songs this is genre, artist, bpm, intensity, volume (and like 15 other audio metrics), etc. And then the program (Spotify) builds a user profile. Based on this profile, the algorithm learns you like songs within the rock genre, at a specific bpm, a specific intensity, volume, etc. And then it will suggest similar songs/artists within the range of that.
#3 is simply a hybrid approach of #1 and #2.
It’s important to note that these algorithms are also learning. The more you interact with them, the more fine-tuned your personalized recommendations would be (to a degree). So if you were recommended band ABC after listening to band XYZ, and you didn’t like ABC (either by ignoring it completely, stopping after a few seconds, or choosing the “don’t recommend me” option if available), the algorithm learns and tries to suggest to you band DEF, which is still somewhat similar to XYZ but with the added info that you don’t like ABC. Etc etc.
But as to what technology yt/fb/IG actually uses, often that is a bit proprietary, and often extremely complex. The approaches above are just very basic and general approaches to recommender systems.
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