private def getLinearRankingParams: ThriftRankingParams = { ThriftRankingParams( `type` = Some(ThriftScoringFunctionType.Linear), minScore = -1.0e100, retweetCountParams = Some(ThriftLinearFeatureRankingParams(weight = 20.0)), replyCountParams = Some(ThriftLinearFeatureRankingParams(weight = 1.0)), reputationParams = Some(ThriftLinearFeatureRankingParams(weight = 0.2)), luceneScoreParams = Some(ThriftLinearFeatureRankingParams(weight = 2.0)), textScoreParams = Some(ThriftLinearFeatureRankingParams(weight = 0.18)), urlParams = Some(ThriftLinearFeatureRankingParams(weight = 2.0)), isReplyParams = Some(ThriftLinearFeatureRankingParams(weight = 1.0)), favCountParams = Some(ThriftLinearFeatureRankingParams(weight = 30.0)), langEnglishUIBoost = 0.5, langEnglishTweetBoost = 0.2, langDefaultBoost = 0.02, unknownLanguageBoost = 0.05, offensiveBoost = 0.1, inTrustedCircleBoost = 3.0, multipleHashtagsOrTrendsBoost = 0.6, inDirectFollowBoost = 4.0, tweetHasTrendBoost = 1.1, selfTweetBoost = 2.0, tweetHasImageUrlBoost = 2.0, tweetHasVideoUrlBoost = 2.0, useUserLanguageInfo = true, ageDecayParams = Some(ThriftAgeDecayRankingParams(slope = 0.005, base = 1.0)) ) }
Twitter's Recommendation Algorithm
741–750 of 1001 posts
Re: Twitter's Recommendation Algorithm
#742The irony is that I prefer Mastodon's sort by time and don't try to be clever approach to this expensive and futile attempt to feed me an endless stream of click bait. I objectively spend more time on Mastodon than on Twitter at this point. It's more engaging for me. It's how Twitter used to work when it was still nice to use. If Twitter wants to put a stop to the user exodus and save lots of money in the process, he…
Re: Twitter's Recommendation Algorithm
#743From 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…
Just removed https://github.com/twitter/the-algorithm/commit/ec83d01dcaeb...
Re: Twitter's Recommendation Algorithm
#744Earlier quoted context omitted.
This sentiment has high correlation to driving conclusions from a very time limited information set. This isn't the only part that is going to be posted to github. What is the net benefit from rushing to condemn something that can only be a net positive compared to the past alternatives? I don't understand the purpose of that approach. Help me.
> can only be a net positive compared to the past alternatives This seems to be unsubstantiated. Are you really claiming that selective disclosure is always superior to complete lack of transparency?
Re: Twitter's Recommendation Algorithm
#745Earlier quoted context omitted.
This shouldn't really be a surprise to anyone. It was reported years ago that Twitter was unable to cut down on hate speech because the automated systems they developed triggered too many [debatably false] positives on Republican politicians and that was bad for the company's reputation. If Twitter wanted to prevent future code changes from impacting that approach, there needed to be something like this in the code o…
[flagged]
Re: Twitter's Recommendation Algorithm
#746Earlier quoted context omitted.
> can only be a net positive compared to the past alternatives This seems to be unsubstantiated. Are you really claiming that selective disclosure is always superior to complete lack of transparency?
Yes. 1>0
Like if I tell you that your boyfriend has been having secret meetings with some woman you don't know, with full knowledge that the secret meetings are because she's a photographer and he's planning to propose, have I improved things by disclosing the information to you in that manner? Were my actions a "net positive"?
Re: Twitter's Recommendation Algorithm
#747Earlier quoted context omitted.
So many unnecessarily cynical takes here. Let's say you were in charge of a large legacy system that some segment of customers complain about it not working for them as well as other segments. How would you know whether their complaints are valid unless you measured it? You have to know first. So measure it.
Yeah, but then what do you do after you measure it? Nothing? No, you make decisions differently so as not to offend whoever is part of the criteria. For example, can we agree that we don't want an "author_is_flat_earther" flag? Because who gives a shit if Twitter makes a change to their recommendation engine that negatively affects flag earthers? Just because something is only used for A/B testing doesn't make it com…
Re: Twitter's Recommendation Algorithm
#748Earlier quoted context omitted.
[flagged]
I would agree the quality of posts has gone downhill, and is way too political for my liking, but there's not much other place to go that isn't elitist.
Re: Twitter's Recommendation Algorithm
#749It'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...