zenogantner
Joined 4 years ago
Joined 4 years ago
btw, "kernel smoothing" etc. are still fairly complex terms that sound a bit like math magic: 1-nearest-neighbor (1-nn), k-nearest-neighbor (knn), and weighted knn would be even simpler and possibly more descriptive.
For example: https://en.wikipedia.org/wiki/%2750s_progression
Instead, it is likely a component that powers ByteDance's commercial recommender system solution, which they market to e-commerce companies: https://www.byteplus.com/en/product/recommend
This was mentioned in past discussions of the paper on HN.
And even if aspects of this are used for TikTok: (a) it would be just one of many components of their recommendation system, and (b) the TikTok recommendation system has changed a lot during the 2+ years since this has been published.
So take what you see here with a grain of salt. After reading the paper and the code, you will NOT know how TikTok's recommendations work.
Like most problems in IT in general :-)
That said, the most interesting lessons are in the first and sixth (the 2nd 6th, the actual 6th) item: How to do a better/more widely scoped job than what you got hired for (by understanding how interests, incentives and responsibilities align in an org) and the fact that in most places, most people are not serious (meaning they tend to not go deeper, look at the big picture, etc.).
Thank you for sharing it, Adam!
- one page with reasonably looking/appealing structure, tables, bullet points, etc.
- fairly long
- looking more closely, things start to be quite generic, lacking nuance ...
The issue I have with this is not that it is agent-written or -drafted, but rather that the signal-to-noise ratio seems to be off.