The Reason TikTok's Algorithmic Recommendation Engine Is So Effective

TikTok's recommendation engine is algorithm-friendly rather than user-friendly, evaluating a number of indicators which may be more mathematically important than whether the user likes a subject or not. JL

Trung Phan reports in The Hustle:

Typically, UX design is meant to be user-friendly. To improve its algorithm, TikTok has made its product a bit less user-friendly, an “algorithm-friendly” design. To get the most valuable inputs possible for its algorithm, TikTok’s design is unique: only one video at a time with a number of indicators as to whether or not the user likes it (length of viewing, re-watches, likes, comments, song choice, video subject, shares). With such clear signals TikTok can quickly understand a user’s preference (which) creates a tight feedback loop that continually improves TikTok’s recommendations

A former product exec at Amazon and Oculus — has penned the most concise explanation of what makes TikTok tick (sorry).

His entry is a must-read for anyone trying to understand TikTok’s recommendation engine (AKA the For Your Page [FYP]).

The secret?

An “algorithm-friendly” design.

Here are some key takeaways:

1. TikTok’s actual machine learning (ML) recommendation algorithm isn’t out of the ordinary
2. However, the data inputs into TikTok’s algorithm are differentiated and — all things equal — better data inputs create better algorithms
3. To get the most valuable inputs possible for its algorithm, TikTok’s design is very unique: It is only one video at a time with a number of indicators as to whether or not the user likes it (length of viewing, re-watches, likes, comments, song choice, video subject, shares)
4. Typically, UX design is meant to be user-friendly. However, to improve its algorithm, TikTok has made its product a bit less user-friendly (users scrolling through multiple pieces of content is a more frictionless experience than just a single video view)
5. Eugene calls this product decision an “algorithm-friendly” design
6. Compare this with a traditional social feed (Twitter, Facebook), both of which offer an endless scroll of content. The user inputs are less clear (“liking” something doesn’t transmit a ton of information)
7. With such clear signals — whether positive or negative — TikTok can quickly understand a user’s preference and serve up more similar content
8. This creates a tight feedback loop and kicks off the flywheel that continually improves TikTok’s recommendations and data inputs