From https://github.com/twitter/the-algorithm/blob/7f90d0ca342b92... ( "author_is_elon", candidate => candidate .getOrElse(AuthorIdFeature, None).contains(candidate.getOrElse(DDGStatsElonFeature, 0L))), ( "author_is_power_user", candidate => candidate .getOrElse(AuthorIdFeature, None) .exists(candidate.getOrElse(DDGStatsVitsFeature, Set.empty[Long]).contains)), ( "author_is_democrat", candidate => candidate .getOrEls…
Twitter's Recommendation Algorithm
621–630 of 1001 posts
Re: Twitter's Recommendation Algorithm
#622Earlier quoted context omitted.
Also how about.... everyone else in the world who is not an American voter?
Before I deliberately locked myself out of it (well before Musk), I asked for my data. They classify me as: * speaks Indonesian Interested in: * Beer * Cricket * DJs * Dance * Enterprise software * Horror * NFL football * South America And aged either between 13-54 or (and?) over 65 Other than the age (I'm neither under 13 nor between 55-64), everything I've listed is incorrect. On that basis, they'd probably call me…
What a strange age classification — no idea about what your age actually is, just that it definitely isn't 55-64. I assume it would never conclude you are under 13 for legal reasons.
Re: Twitter's Recommendation Algorithm
#623Earlier quoted context omitted.
This is a very cynical take. They should be commended for publishing recommendation code at all, which no other major social network does.
I still hear reverse-FUD about nvidia supposedly fully open-sourcing their Linux driver, when in reality they opened a tiny kernel portion of it that allows the main proprietary blob to connect to necessary kernel interfaces. You have to call out this bullshit when you see it.
Wait, what? AFAIU what you say is true, except for the part where the “main proprietary blob” does not run on the CPU. This isn’t as glorious as an actual open-source driver would be, but it does have meaningful advantages—e.g. you now have a ghost of a chance of implementing Nvidia GPU support on a non-Linux kernel, by uploading the GPU-side blob and rewriting the CPU-side shim as required. Or is the blob license-restricted from being used line that?
Re: Twitter's Recommendation Algorithm
#624From https://github.com/twitter/the-algorithm/blob/7f90d0ca342b92... ( "author_is_elon", candidate => candidate .getOrElse(AuthorIdFeature, None).contains(candidate.getOrElse(DDGStatsElonFeature, 0L))), ( "author_is_power_user", candidate => candidate .getOrElse(AuthorIdFeature, None) .exists(candidate.getOrElse(DDGStatsVitsFeature, Set.empty[Long]).contains)), ( "author_is_democrat", candidate => candidate .getOrEls…
github link now show a warning at the top of the page: > This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Is this new? Perhaps Twitter already removed the code from their main branch? Or was this just a joke from the beginning?
Yup, they removed it.
https://github.com/twitter/the-algorithm/commit/ec83d01dcaeb...
Here's what Musk said:
> But we are deleting this bs. I only learned about it now! Will be gone by tomorrow.
Re: Twitter's Recommendation Algorithm
#625Context: I teach at Princeton and study social media and recommendation systems. From a very quick skim of the repositories, this appears to be quite limited transparency. The documentation gives a decent high-level overview of how Tweet recommendation works—no surprises—and the code tracks that roadmap. Those are meaningful positive steps. But the underlying policies and models are almost entirely missing (there are…
Wouldn’t that make them easy prey of “spam SEO”. However, given the framework isn’t it still possible to guess the models?
The spam SEO issue should be dealt/thought about _before_ engaging in the whole adventure, and having to guess how it could work if decently implemented properly defeats the "open source" spirit of it.
More credits would be given if the very idea of open sourcing the algorithm hasn't already been discussed to death with predictions of the difficult points and how it probably won't happen in any sane way.
Re: Twitter's Recommendation Algorithm
#626Re: Twitter's Recommendation Algorithm
#627Earlier quoted context omitted.
I believe LeBron James said recently he isn't going to waste his money on a blue checkmark, so it should be interesting to see what stays and what goes.
LeBron doesn’t get $84 of value from Twitter? Definitely not a political statement going on there.
He may well, but he may have also concluded that the indirect cost of having a blue tick outweighs the benefits.
Re: Twitter's Recommendation Algorithm
#628Earlier quoted context omitted.
American's lax attitude towards cultivating more than two parties is literally killing the republic from the inside.
In some other countries the different interest groups sort themselves into two factions after being elected but I don't know that it is really that different in practice.
In some ways it might be worse. Here we had a party that promised to vote with one faction and partly thanks to it survived the election. A few months later they did a 180 and voted the other faction into government. Opinions aside, imagine voting for Sanders only for him to elect Trump or the other way around. With that said, two party version also comes with major flaws.
Re: Twitter's Recommendation Algorithm
#629It's reassuring to know that billion dollar tech companies write CI exactly like I do: https://github.com/twitter/the-algorithm/blob/main/ci/ci.sh Permalink: https://github.com/twitter/the-algorithm/blob/7f90d0ca342b92...
Maybe it's weird, but for all the work I have ever done, I have never used CI/CD in the way that it was meant to be used, or never really leveraged it. Maybe all of my past jobs were unprofessional, but like, I see a lot of jobs using "CI/CD experience required" and I think... huh I wonder if they actually do it
I'd expect what the CI/CD typically does is split into different parts of your process ?
I also had jobs where CI/CD were completely accessory, but in exchange we were doing a lot more work on our local machines and deploy pipelines.
In comparison I currently see repos where remote editing files straight from gitlab and merging the changes is enough to get everything shipped.
I see it as a difference in philosophy more than what's best or "professional".
Re: Twitter's Recommendation Algorithm
#630Earlier quoted context omitted.
Do you think the code looked like that prior to Elon's purchase? I suspect that there was another name there before. Separately, which of these groups do you think that they use as a control?
> which of these groups do you think that they use as a control? When you run an A/B test you randomly divide your users into groups, one (treatment) getting the new behavior and one (control) getting the current production behavior. So your question doesn't make much sense?
Depends on the question. If you want to answer a question like “does change X increase engagement?” then a straight A/B test works. But if you want to answer one like “does change X increase engagement while (favoring/not favoring) group 1 over group 2?”, then an A/B test plus measuring groups 1 and 2 will not work without a control group, because without controls you don’t know if any changes to engagement for your measured groups are significant. There is some threshold of change to the engagement for the measured groups which is too small to be significant, and you should ignore results that only measure that noise.