5 ms·
For ranking the candidates these predictions are combined into a score by weighting them: "recap.engagement.is_favorited": 0.5 "recap.engagement.is_g
by roddylindsay 4y ago
For ranking the candidates these predictions are combined into a score by
weighting them:
"recap.engagement.is_favorited": 0.5
"recap.engagement.is_good_clicked_convo_desc_favorited_or_replied": 11* (the
maximum prediction from these two "good click" features is used and weighted by
11, the other prediction is ignored).
"recap.engagement.is_good_clicked_convo_desc_v2": 11*
"recap.engagement.is_negative_feedback_v2": -74
"recap.engagement.is_profile_clicked_and_profile_engaged": 12
"recap.engagement.is_replied": 27
"recap.engagement.is_replied_reply_engaged_by_author": 75
"recap.engagement.is_report_tweet_clicked": -369
"recap.engagement.is_retweeted": 1 "recap.engagement.is_video_playback_50": 0.005
Who set those weights, and why were they chosen?
- bobbygoodlatte 4y ago"recap.engagement.is_replied": 27 "recap.engagement.is_replied_reply_engaged_by_author": 75 I wonder if this is why threads rank so obnoxiously high. They get artificially boosted by the author replying to their own tweet
- localplume 4y agoisn't that the author replying to a reply on their tweet? so its promoting positive discussion, hence pushing the engagement higher?
- Mehdi2277 4y agoHaving worked at similar companies on similar systems usually A/B experiments and smaller probability of an action bigger weight it must have to matter much overall. The constants are generally done through some ab tests to get them into reasonable overall behavior but they are a pain to tune and very unlikely optimal in any real sense as it’s often too difficult to do extensive search of them. Like often I’ll see new target have a couple different weights tried on an ab and then maybe second set of experiments after rough magnitude is determined.
- dmak 4y agoCould you link to the code on github?