The Netflix Prize was interesting, and drew a lot of attention to it, but ultimately wasn't that simply stuffed into the trash bin? Not long after that, Netflix basically abandoned both user ratings of significance, and realistic recommendations. Now it's just a nonsense engine with some sort of meaningless overlap or whatever they call it.

I guess they realised giving user swhat they want isn't necessarily profitable for business.

> into the trash bin?

They did not implement the winning solution as is, but a lot of good stuff came from the winning teams, including techniques (such as SVD) that are in use as of today (or maybe a few years back).

> just a nonsense engine

No, it is safe to assume the recommendation engine of Netflix is close to the state-of-the-art. A lot of money and talent went into it.

"They did not implement the winning solution as is"

Shortly after that contest finished Netflix removed the five star rating system, and dramatically subdued the recommendation engine. Now the vast majority of the content surfacing is universal beyond some small category filtering (e.g. You like crime dramas and horrors so here's a bunch of the most popular stuff from those categories).

Now they have a "match" rating that I have not met a single person who finds useful (it is almost at the point of farce and seems more like a randomization engine).

A lot of money and talent went into something that Netflix clearly decided just wasn't important or useful enough for them. Now they just push Don't F*ck With Cats on everyone.

> Now it’s just people gaming Kaggle to get a job.

How is that different from "LeetCode PhD" doing "DP Hard" for 6 months to get into FAANG?

Feels like you just answered your own question. (Which happens a lot on HN)

I don't see a problem with that. It seems to me most companies sponsoring the contests have legitimate problems they wanted solved.

Now it really illustrated to me how data science is still an incipient field. Even the basic Titanic example has a lot of fake solution and a bit of "cheating" (overfitting).

And don't get me started on the multitude of tutorials and examples that only show the loss decreasing graph but the actual results are not evaluated (and thus might have some glaring defects).

The Netflix Prize winners regularly compete on Kaggle. One of the top 10 Netflix Prize winners joined Kaggle early on (he got a job out of it). Kaggle competitions are a game. Most notorious players already have a good job.

I think the original intent was great. And you can learn from it still. However, as the job market for data scientists heated up, Kaggle became something else entirely.

One of the Kaggle competitions was used as an assignment for the Statistics course at my university. IIRC it was really fiddly.

It's pretty common these days, many courses at top universities feature a Kaggle competition/datasets.