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
Having 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.
